# Limpiamos el entorno de Trabajo
rm(list=ls())
# Limpiamos la consola
cat("\014")# Comprobamos que está bien establecido el directorio
getwd()## [1] "/home/oscar/Documentos/R/menstrual"dir()## [1] "data.csv" "header.html"
## [3] "mcb_separado.csv" "mcb_separado.xls"
## [5] "mcbackup_files" "mcbackup.csv"
## [7] "mcbackup.html" "mcbackup.json"
## [9] "mcbackup.Rmd" "menstrual-jupyter.ipynb"
## [11] "menstrual.ipynb" "Untitled.ipynb"
## [13] "Untitled1.ipynb" "Untitled2.ipynb"#indicamos el directorio de trabajo
setwd("~/Documentos/R/menstrual")# Importamos las librerias a utilizar
packages <- c( "magrittr", "jsonlite", "tidyverse", "prettyR", "git2r","funModeling", "zoo", "lubridate", "GGally", "ggfortify", "ggplot2", "modeest")
newpack = packages[!(packages %in% installed.packages()[,"Package"])]
if(length(newpack)) install.packages(newpack)
a=lapply(packages, library, character.only=TRUE)library(rjson)
# You can pass directly the filename
my.JSON <- fromJSON(file="mcbackup.json")Genero las listas de cada elemento
settings <- my.JSON$settings
event <- my.JSON$event
measure <- data.frame(my.JSON$measure)
notification<- data.frame(my.JSON$notification)
symptom<- data.frame(my.JSON$symptom)
day <- data.frame(my.JSON$day)
value <- data.frame(my.JSON$value)Genero cada dataframe
json_file_settings <- lapply(my.JSON$settings, function(x) {
x[sapply(x, is.null)] <- NA
unlist(x)
})
settings <-as.data.frame(t(do.call("cbind", json_file_settings)))json_file_day <- lapply(my.JSON$day, function(x) {
x[sapply(x, is.null)] <- NA
unlist(x)
})
day <-as.data.frame(t(do.call("cbind", json_file_day)))json_file_value <- lapply(my.JSON$value, function(x) {
x[sapply(x, is.null)] <- NA
unlist(x)
})
value <-as.data.frame(t(do.call("cbind", json_file_value)))json_file_symptom <- lapply(my.JSON$symptom, function(x) {
x[sapply(x, is.null)] <- NA
unlist(x)
})
symptom <-as.data.frame(t(do.call("cbind", json_file_symptom)))json_file_notification <- lapply(my.JSON$notification, function(x) {
x[sapply(x, is.null)] <- NA
unlist(x)
})
notification <- as.data.frame(t(do.call("cbind", json_file_notification)))json_file_event <- lapply(my.JSON$event, function(x) {
x[sapply(x, is.null)] <- NA
unlist(x)
})
event <-as.data.frame(t(do.call("cbind", json_file_event)))json_file_measure <- lapply(my.JSON$measure, function(x) {
x[sapply(x, is.null)] <- NA
unlist(x)
})
measure <-as.data.frame(t(do.call("cbind", json_file_measure)))Eliminamos objetos innecesarioss
rm(json_file_day,json_file_event,json_file_measure,json_file_notification,json_file_symptom,json_file_value,a,my.JSON,json_file_settings)Cambiamos el nombre del id
simptoma <- symptom %>%
rename(symptom_id = id)
valor <- value %>%
rename(value_id = id)Fusionamos 2 tablas
evento <- left_join(event, valor, by = "value_id")
measures <- merge(simptoma, measure, by = "symptom_id", all = TRUE)
data_raw1 <- merge(measures, evento, by= "symptom_id", all = TRUE)
rm(symptom,simptoma,value,valor,measures,measure,settings)
data_raw2 <- merge(data_raw1, notification, by = "symptom_id", all = TRUE)
rm(event,notification,evento,setting,data_raw1)
data_raw <- merge(data_raw2,day, by= "date", all = TRUE)
rm(day, data_raw2, settings)EDA_basico <- function(data)
{
str(data)
dim(data)
colnames(data)
summary(data)
# describe(data) #library(prettyR)
}EDA_basico(data_raw)## 'data.frame': 855 obs. of 26 variables:
## $ date : chr "2012-09-09" "2013-05-17" "2013-05-29" "2013-05-31" ...
## $ symptom_id: chr "bdd8fb86-4cbf-4832-b975-742cd3109215" "c8c60842-b805-41ba-b7b9-d772e39ff3b5" "0239f552-c6b3-42ff-8005-0d45ea1f8962" "c8c60842-b805-41ba-b7b9-d772e39ff3b5" ...
## $ name.x : chr "Menstruación" "Citas médicas" "Relación sexual" "Citas médicas" ...
## $ num.x : chr "-9999" "14" "0" "14" ...
## $ mode : chr "0" "0" "0" "0" ...
## $ type : chr "1" "1" "4" "1" ...
## $ layout : chr "3" "7" "2" "7" ...
## $ chart : chr "1" "0" "0" "0" ...
## $ category : chr "0" "c8c60842-b805-41ba-b7b9-d772e39ff3b5" "0239f552-c6b3-42ff-8005-0d45ea1f8962" "c8c60842-b805-41ba-b7b9-d772e39ff3b5" ...
## $ id.x : chr NA NA NA NA ...
## $ datetime : chr NA NA NA NA ...
## $ value : chr NA NA NA NA ...
## $ value_id : chr "23d86cea-d536-4343-ab59-a1bb86aec86e" "cc56c587-77d5-489e-a650-f7814c6f30b0" "b3128bcd-b057-4de1-b776-147dc6edb677" "cc56c587-77d5-489e-a650-f7814c6f30b0" ...
## $ deleted : chr "2012-09-09" "2013-05-17" "2013-05-29" "2013-05-31" ...
## $ name.y : chr "Moderada" "Obstetricia y Ginecología" "Sin protección" "Obstetricia y Ginecología" ...
## $ num.y : chr "1" "1" "0" "1" ...
## $ icon : chr "-2" "21" "32" "21" ...
## $ color : chr "-65536" "-1" "-65536" "-1" ...
## $ id.y : chr NA NA NA NA ...
## $ label : chr NA NA NA NA ...
## $ hour : chr NA NA NA NA ...
## $ minute : chr NA NA NA NA ...
## $ when : chr NA NA NA NA ...
## $ delay : chr NA NA NA NA ...
## $ active : chr NA NA NA NA ...
## $ note : chr NA NA NA NA ...## date symptom_id name.x num.x
## Length:855 Length:855 Length:855 Length:855
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## mode type layout chart
## Length:855 Length:855 Length:855 Length:855
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## category id.x datetime value
## Length:855 Length:855 Length:855 Length:855
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## value_id deleted name.y num.y
## Length:855 Length:855 Length:855 Length:855
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## icon color id.y label
## Length:855 Length:855 Length:855 Length:855
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## hour minute when delay
## Length:855 Length:855 Length:855 Length:855
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## active note
## Length:855 Length:855
## Class :character Class :character
## Mode :character Mode :character# Valores vacíos
print("Mostrar variables con campos na")## [1] "Mostrar variables con campos na"colSums(is.na(data_raw))## date symptom_id name.x num.x mode type layout
## 132 18 19 19 19 19 19
## chart category id.x datetime value value_id deleted
## 19 19 738 738 738 150 150
## name.y num.y icon color id.y label hour
## 150 150 150 150 852 852 852
## minute when delay active note
## 852 852 852 852 790print("Mostrar variables con datos vacios")## [1] "Mostrar variables con datos vacios"colSums(data_raw=="")## date symptom_id name.x num.x mode type layout
## NA NA NA NA NA NA NA
## chart category id.x datetime value value_id deleted
## NA NA NA NA NA NA NA
## name.y num.y icon color id.y label hour
## NA NA NA NA NA NA NA
## minute when delay active note
## NA NA NA NA NAprint("Valores con integrogación")## [1] "Valores con integrogación"colSums(data_raw==" ?")## date symptom_id name.x num.x mode type layout
## NA NA NA NA NA NA NA
## chart category id.x datetime value value_id deleted
## NA NA NA NA NA NA NA
## name.y num.y icon color id.y label hour
## NA NA NA NA NA NA NA
## minute when delay active note
## NA NA NA NA NA#print("Mostrar variables con campos na")
#which (is.na(data_raw))
print("Es cierto que hay valores na?")## [1] "Es cierto que hay valores na?"any(is.na(data_raw))## [1] TRUEprint("Suma valores na")## [1] "Suma valores na"sum(is.na(data_raw))## [1] 10151data <- dplyr::select(data_raw, -id.y, -label, -hour, -minute, -when, -delay, -active, -note, -deleted)aed_basico <- function(data)
{
glimpse(data)
status(data) #library(git2r)
head(freq(data))
profiling_num(as.numeric(data))
plot_num(data)
describe(data)
dim(data)
summary(data)
colnames(data)
}#aed_basico((data))glimpse(data)## Rows: 855
## Columns: 17
## $ date <chr> "2012-09-09", "2013-05-17", "2013-05-29", "2013-05-31", "2…
## $ symptom_id <chr> "bdd8fb86-4cbf-4832-b975-742cd3109215", "c8c60842-b805-41b…
## $ name.x <chr> "Menstruación", "Citas médicas", "Relación sexual", "Citas…
## $ num.x <chr> "-9999", "14", "0", "14", "0", "0", "14", "0", "14", "0", …
## $ mode <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"…
## $ type <chr> "1", "1", "4", "1", "4", "4", "1", "4", "1", "4", "4", "1"…
## $ layout <chr> "3", "7", "2", "7", "2", "2", "7", "2", "7", "2", "2", "3"…
## $ chart <chr> "1", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "1"…
## $ category <chr> "0", "c8c60842-b805-41ba-b7b9-d772e39ff3b5", "0239f552-c6b…
## $ id.x <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ datetime <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ value <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ value_id <chr> "23d86cea-d536-4343-ab59-a1bb86aec86e", "cc56c587-77d5-489…
## $ name.y <chr> "Moderada", "Obstetricia y Ginecología", "Sin protección",…
## $ num.y <chr> "1", "1", "0", "1", "0", "0", "1", "0", "1", "1", "1", "0"…
## $ icon <chr> "-2", "21", "32", "21", "32", "32", "21", "32", "21", "32"…
## $ color <chr> "-65536", "-1", "-65536", "-1", "-65536", "-65536", "-1", …status(data) #library(git2r)## variable q_zeros p_zeros q_na p_na q_inf p_inf type unique
## 1 date 0 0.000000000 132 0.15438596 0 0 character 635
## 2 symptom_id 1 0.001169591 18 0.02105263 0 0 character 28
## 3 name.x 0 0.000000000 19 0.02222222 0 0 character 27
## 4 num.x 308 0.360233918 19 0.02222222 0 0 character 27
## 5 mode 719 0.840935673 19 0.02222222 0 0 character 2
## 6 type 0 0.000000000 19 0.02222222 0 0 character 9
## 7 layout 191 0.223391813 19 0.02222222 0 0 character 19
## 8 chart 432 0.505263158 19 0.02222222 0 0 character 22
## 9 category 182 0.212865497 19 0.02222222 0 0 character 27
## 10 id.x 0 0.000000000 738 0.86315789 0 0 character 117
## 11 datetime 0 0.000000000 738 0.86315789 0 0 character 117
## 12 value 0 0.000000000 738 0.86315789 0 0 character 62
## 13 value_id 0 0.000000000 150 0.17543860 0 0 character 25
## 14 name.y 0 0.000000000 150 0.17543860 0 0 character 18
## 15 num.y 402 0.470175439 150 0.17543860 0 0 character 5
## 16 icon 0 0.000000000 150 0.17543860 0 0 character 9
## 17 color 0 0.000000000 150 0.17543860 0 0 character 9head(freq(data)) ## date frequency percentage cumulative_perc
## 1 <NA> 132 15.44 15.44
## 2 2014-10-29 3 0.35 15.79
## 3 2014-12-28 3 0.35 16.14
## 4 2015-01-14 3 0.35 16.49
## 5 2015-05-05 3 0.35 16.84
## 6 2016-02-10 3 0.35 17.19
## 7 2017-12-20 3 0.35 17.54
## 8 2013-06-07 2 0.23 17.77
## 9 2013-08-17 2 0.23 18.00
## 10 2013-09-15 2 0.23 18.23
## 11 2014-09-20 2 0.23 18.46
## 12 2014-09-30 2 0.23 18.69
## 13 2014-10-13 2 0.23 18.92
## 14 2014-10-14 2 0.23 19.15
## 15 2014-10-15 2 0.23 19.38
## 16 2014-10-24 2 0.23 19.61
## 17 2014-10-25 2 0.23 19.84
## 18 2014-10-26 2 0.23 20.07
## 19 2014-10-27 2 0.23 20.30
## 20 2014-10-31 2 0.23 20.53
## 21 2014-11-02 2 0.23 20.76
## 22 2014-11-15 2 0.23 20.99
## 23 2014-11-29 2 0.23 21.22
## 24 2014-12-07 2 0.23 21.45
## 25 2014-12-08 2 0.23 21.68
## 26 2014-12-09 2 0.23 21.91
## 27 2014-12-10 2 0.23 22.14
## 28 2014-12-13 2 0.23 22.37
## 29 2014-12-14 2 0.23 22.60
## 30 2014-12-17 2 0.23 22.83
## 31 2014-12-20 2 0.23 23.06
## 32 2014-12-22 2 0.23 23.29
## 33 2014-12-24 2 0.23 23.52
## 34 2014-12-26 2 0.23 23.75
## 35 2015-01-17 2 0.23 23.98
## 36 2015-01-19 2 0.23 24.21
## 37 2015-01-24 2 0.23 24.44
## 38 2015-01-27 2 0.23 24.67
## 39 2015-02-06 2 0.23 24.90
## 40 2015-02-08 2 0.23 25.13
## 41 2015-02-10 2 0.23 25.36
## 42 2015-02-11 2 0.23 25.59
## 43 2015-02-18 2 0.23 25.82
## 44 2015-02-20 2 0.23 26.05
## 45 2015-03-12 2 0.23 26.28
## 46 2015-03-18 2 0.23 26.51
## 47 2015-03-21 2 0.23 26.74
## 48 2015-03-23 2 0.23 26.97
## 49 2015-03-25 2 0.23 27.20
## 50 2015-03-27 2 0.23 27.43
## 51 2015-04-27 2 0.23 27.66
## 52 2015-04-28 2 0.23 27.89
## 53 2015-05-01 2 0.23 28.12
## 54 2015-05-02 2 0.23 28.35
## 55 2015-05-03 2 0.23 28.58
## 56 2015-05-31 2 0.23 28.81
## 57 2015-06-19 2 0.23 29.04
## 58 2015-07-23 2 0.23 29.27
## 59 2015-07-25 2 0.23 29.50
## 60 2015-07-27 2 0.23 29.73
## 61 2015-08-06 2 0.23 29.96
## 62 2015-08-25 2 0.23 30.19
## 63 2015-09-24 2 0.23 30.42
## 64 2015-12-13 2 0.23 30.65
## 65 2016-02-03 2 0.23 30.88
## 66 2016-02-07 2 0.23 31.11
## 67 2016-02-11 2 0.23 31.34
## 68 2016-02-23 2 0.23 31.57
## 69 2016-02-27 2 0.23 31.80
## 70 2016-03-01 2 0.23 32.03
## 71 2016-03-06 2 0.23 32.26
## 72 2016-05-12 2 0.23 32.49
## 73 2016-05-17 2 0.23 32.72
## 74 2016-07-26 2 0.23 32.95
## 75 2016-10-05 2 0.23 33.18
## 76 2016-12-02 2 0.23 33.41
## 77 2017-01-09 2 0.23 33.64
## 78 2017-12-18 2 0.23 33.87
## 79 2017-12-28 2 0.23 34.10
## 80 2018-04-07 2 0.23 34.33
## 81 2018-07-27 2 0.23 34.56
## 82 2019-08-19 2 0.23 34.79
## 83 2020-02-25 2 0.23 35.02
## 84 2012-09-09 1 0.12 35.14
## 85 2013-05-17 1 0.12 35.26
## 86 2013-05-29 1 0.12 35.38
## 87 2013-05-31 1 0.12 35.50
## 88 2013-06-05 1 0.12 35.62
## 89 2013-06-09 1 0.12 35.74
## 90 2013-06-10 1 0.12 35.86
## 91 2013-07-20 1 0.12 35.98
## 92 2013-07-24 1 0.12 36.10
## 93 2013-07-25 1 0.12 36.22
## 94 2013-07-31 1 0.12 36.34
## 95 2013-08-05 1 0.12 36.46
## 96 2013-08-16 1 0.12 36.58
## 97 2013-08-18 1 0.12 36.70
## 98 2013-08-19 1 0.12 36.82
## 99 2013-08-23 1 0.12 36.94
## 100 2013-08-25 1 0.12 37.06
## 101 2013-08-31 1 0.12 37.18
## 102 2013-09-10 1 0.12 37.30
## 103 2013-09-16 1 0.12 37.42
## 104 2013-09-17 1 0.12 37.54
## 105 2013-09-18 1 0.12 37.66
## 106 2013-09-20 1 0.12 37.78
## 107 2013-09-22 1 0.12 37.90
## 108 2013-10-01 1 0.12 38.02
## 109 2013-10-05 1 0.12 38.14
## 110 2013-10-06 1 0.12 38.26
## 111 2013-10-08 1 0.12 38.38
## 112 2014-07-01 1 0.12 38.50
## 113 2014-08-04 1 0.12 38.62
## 114 2014-08-05 1 0.12 38.74
## 115 2014-08-06 1 0.12 38.86
## 116 2014-09-07 1 0.12 38.98
## 117 2014-09-09 1 0.12 39.10
## 118 2014-09-11 1 0.12 39.22
## 119 2014-09-12 1 0.12 39.34
## 120 2014-09-13 1 0.12 39.46
## 121 2014-09-16 1 0.12 39.58
## 122 2014-09-17 1 0.12 39.70
## 123 2014-09-18 1 0.12 39.82
## 124 2014-09-19 1 0.12 39.94
## 125 2014-09-22 1 0.12 40.06
## 126 2014-09-24 1 0.12 40.18
## 127 2014-09-25 1 0.12 40.30
## 128 2014-09-26 1 0.12 40.42
## 129 2014-09-27 1 0.12 40.54
## 130 2014-09-28 1 0.12 40.66
## 131 2014-09-29 1 0.12 40.78
## 132 2014-10-02 1 0.12 40.90
## 133 2014-10-04 1 0.12 41.02
## 134 2014-10-06 1 0.12 41.14
## 135 2014-10-09 1 0.12 41.26
## 136 2014-10-10 1 0.12 41.38
## 137 2014-10-11 1 0.12 41.50
## 138 2014-10-12 1 0.12 41.62
## 139 2014-10-16 1 0.12 41.74
## 140 2014-10-17 1 0.12 41.86
## 141 2014-10-18 1 0.12 41.98
## 142 2014-10-19 1 0.12 42.10
## 143 2014-10-21 1 0.12 42.22
## 144 2014-10-28 1 0.12 42.34
## 145 2014-10-30 1 0.12 42.46
## 146 2014-11-01 1 0.12 42.58
## 147 2014-11-06 1 0.12 42.70
## 148 2014-11-07 1 0.12 42.82
## 149 2014-11-08 1 0.12 42.94
## 150 2014-11-10 1 0.12 43.06
## 151 2014-11-13 1 0.12 43.18
## 152 2014-11-17 1 0.12 43.30
## 153 2014-11-18 1 0.12 43.42
## 154 2014-11-22 1 0.12 43.54
## 155 2014-11-26 1 0.12 43.66
## 156 2014-12-04 1 0.12 43.78
## 157 2014-12-06 1 0.12 43.90
## 158 2014-12-11 1 0.12 44.02
## 159 2014-12-12 1 0.12 44.14
## 160 2014-12-15 1 0.12 44.26
## 161 2014-12-16 1 0.12 44.38
## 162 2014-12-18 1 0.12 44.50
## 163 2014-12-21 1 0.12 44.62
## 164 2014-12-23 1 0.12 44.74
## 165 2014-12-25 1 0.12 44.86
## 166 2014-12-27 1 0.12 44.98
## 167 2014-12-29 1 0.12 45.10
## 168 2014-12-30 1 0.12 45.22
## 169 2014-12-31 1 0.12 45.34
## 170 2015-01-01 1 0.12 45.46
## 171 2015-01-04 1 0.12 45.58
## 172 2015-01-05 1 0.12 45.70
## 173 2015-01-08 1 0.12 45.82
## 174 2015-01-10 1 0.12 45.94
## 175 2015-01-11 1 0.12 46.06
## 176 2015-01-12 1 0.12 46.18
## 177 2015-01-20 1 0.12 46.30
## 178 2015-01-22 1 0.12 46.42
## 179 2015-01-26 1 0.12 46.54
## 180 2015-01-28 1 0.12 46.66
## 181 2015-01-29 1 0.12 46.78
## 182 2015-01-30 1 0.12 46.90
## 183 2015-02-01 1 0.12 47.02
## 184 2015-02-02 1 0.12 47.14
## 185 2015-02-03 1 0.12 47.26
## 186 2015-02-04 1 0.12 47.38
## 187 2015-02-05 1 0.12 47.50
## 188 2015-02-07 1 0.12 47.62
## 189 2015-02-09 1 0.12 47.74
## 190 2015-02-13 1 0.12 47.86
## 191 2015-02-16 1 0.12 47.98
## 192 2015-02-19 1 0.12 48.10
## 193 2015-02-21 1 0.12 48.22
## 194 2015-02-22 1 0.12 48.34
## 195 2015-02-23 1 0.12 48.46
## 196 2015-02-24 1 0.12 48.58
## 197 2015-02-27 1 0.12 48.70
## 198 2015-03-01 1 0.12 48.82
## 199 2015-03-03 1 0.12 48.94
## 200 2015-03-06 1 0.12 49.06
## 201 2015-03-07 1 0.12 49.18
## 202 2015-03-08 1 0.12 49.30
## 203 2015-03-09 1 0.12 49.42
## 204 2015-03-10 1 0.12 49.54
## 205 2015-03-11 1 0.12 49.66
## 206 2015-03-13 1 0.12 49.78
## 207 2015-03-14 1 0.12 49.90
## 208 2015-03-15 1 0.12 50.02
## 209 2015-03-16 1 0.12 50.14
## 210 2015-03-19 1 0.12 50.26
## 211 2015-03-20 1 0.12 50.38
## 212 2015-03-22 1 0.12 50.50
## 213 2015-03-24 1 0.12 50.62
## 214 2015-03-26 1 0.12 50.74
## 215 2015-03-28 1 0.12 50.86
## 216 2015-04-02 1 0.12 50.98
## 217 2015-04-03 1 0.12 51.10
## 218 2015-04-07 1 0.12 51.22
## 219 2015-04-10 1 0.12 51.34
## 220 2015-04-11 1 0.12 51.46
## 221 2015-04-12 1 0.12 51.58
## 222 2015-04-13 1 0.12 51.70
## 223 2015-04-14 1 0.12 51.82
## 224 2015-04-15 1 0.12 51.94
## 225 2015-04-19 1 0.12 52.06
## 226 2015-04-20 1 0.12 52.18
## 227 2015-04-21 1 0.12 52.30
## 228 2015-04-22 1 0.12 52.42
## 229 2015-04-23 1 0.12 52.54
## 230 2015-04-26 1 0.12 52.66
## 231 2015-04-29 1 0.12 52.78
## 232 2015-04-30 1 0.12 52.90
## 233 2015-05-06 1 0.12 53.02
## 234 2015-05-08 1 0.12 53.14
## 235 2015-05-10 1 0.12 53.26
## 236 2015-05-11 1 0.12 53.38
## 237 2015-05-12 1 0.12 53.50
## 238 2015-05-13 1 0.12 53.62
## 239 2015-05-15 1 0.12 53.74
## 240 2015-05-21 1 0.12 53.86
## 241 2015-05-22 1 0.12 53.98
## 242 2015-05-23 1 0.12 54.10
## 243 2015-05-27 1 0.12 54.22
## 244 2015-05-28 1 0.12 54.34
## 245 2015-05-29 1 0.12 54.46
## 246 2015-06-01 1 0.12 54.58
## 247 2015-06-02 1 0.12 54.70
## 248 2015-06-04 1 0.12 54.82
## 249 2015-06-16 1 0.12 54.94
## 250 2015-06-20 1 0.12 55.06
## 251 2015-06-21 1 0.12 55.18
## 252 2015-06-22 1 0.12 55.30
## 253 2015-06-23 1 0.12 55.42
## 254 2015-06-28 1 0.12 55.54
## 255 2015-06-30 1 0.12 55.66
## 256 2015-07-03 1 0.12 55.78
## 257 2015-07-04 1 0.12 55.90
## 258 2015-07-08 1 0.12 56.02
## 259 2015-07-10 1 0.12 56.14
## 260 2015-07-12 1 0.12 56.26
## 261 2015-07-16 1 0.12 56.38
## 262 2015-07-17 1 0.12 56.50
## 263 2015-07-20 1 0.12 56.62
## 264 2015-07-21 1 0.12 56.74
## 265 2015-07-22 1 0.12 56.86
## 266 2015-07-24 1 0.12 56.98
## 267 2015-07-26 1 0.12 57.10
## 268 2015-07-28 1 0.12 57.22
## 269 2015-08-08 1 0.12 57.34
## 270 2015-08-10 1 0.12 57.46
## 271 2015-08-12 1 0.12 57.58
## 272 2015-08-18 1 0.12 57.70
## 273 2015-08-21 1 0.12 57.82
## 274 2015-08-24 1 0.12 57.94
## 275 2015-08-26 1 0.12 58.06
## 276 2015-08-27 1 0.12 58.18
## 277 2015-09-05 1 0.12 58.30
## 278 2015-09-12 1 0.12 58.42
## 279 2015-09-15 1 0.12 58.54
## 280 2015-09-16 1 0.12 58.66
## 281 2015-09-17 1 0.12 58.78
## 282 2015-09-18 1 0.12 58.90
## 283 2015-09-19 1 0.12 59.02
## 284 2015-09-22 1 0.12 59.14
## 285 2015-09-23 1 0.12 59.26
## 286 2015-09-25 1 0.12 59.38
## 287 2015-09-26 1 0.12 59.50
## 288 2015-09-27 1 0.12 59.62
## 289 2015-09-28 1 0.12 59.74
## 290 2015-09-30 1 0.12 59.86
## 291 2015-10-04 1 0.12 59.98
## 292 2015-10-05 1 0.12 60.10
## 293 2015-10-06 1 0.12 60.22
## 294 2015-10-13 1 0.12 60.34
## 295 2015-10-17 1 0.12 60.46
## 296 2015-10-24 1 0.12 60.58
## 297 2015-10-29 1 0.12 60.70
## 298 2015-10-31 1 0.12 60.82
## 299 2015-11-09 1 0.12 60.94
## 300 2015-11-15 1 0.12 61.06
## 301 2015-11-22 1 0.12 61.18
## 302 2015-11-26 1 0.12 61.30
## 303 2015-12-03 1 0.12 61.42
## 304 2015-12-06 1 0.12 61.54
## 305 2015-12-09 1 0.12 61.66
## 306 2015-12-10 1 0.12 61.78
## 307 2015-12-11 1 0.12 61.90
## 308 2015-12-12 1 0.12 62.02
## 309 2015-12-15 1 0.12 62.14
## 310 2015-12-21 1 0.12 62.26
## 311 2015-12-24 1 0.12 62.38
## 312 2015-12-26 1 0.12 62.50
## 313 2015-12-27 1 0.12 62.62
## 314 2015-12-29 1 0.12 62.74
## 315 2016-01-03 1 0.12 62.86
## 316 2016-01-09 1 0.12 62.98
## 317 2016-01-10 1 0.12 63.10
## 318 2016-01-13 1 0.12 63.22
## 319 2016-01-14 1 0.12 63.34
## 320 2016-01-15 1 0.12 63.46
## 321 2016-01-16 1 0.12 63.58
## 322 2016-01-18 1 0.12 63.70
## 323 2016-01-19 1 0.12 63.82
## 324 2016-01-21 1 0.12 63.94
## 325 2016-01-22 1 0.12 64.06
## 326 2016-01-25 1 0.12 64.18
## 327 2016-01-26 1 0.12 64.30
## 328 2016-01-28 1 0.12 64.42
## 329 2016-01-29 1 0.12 64.54
## 330 2016-01-30 1 0.12 64.66
## 331 2016-01-31 1 0.12 64.78
## 332 2016-02-01 1 0.12 64.90
## 333 2016-02-02 1 0.12 65.02
## 334 2016-02-04 1 0.12 65.14
## 335 2016-02-05 1 0.12 65.26
## 336 2016-02-06 1 0.12 65.38
## 337 2016-02-09 1 0.12 65.50
## 338 2016-02-12 1 0.12 65.62
## 339 2016-02-13 1 0.12 65.74
## 340 2016-02-14 1 0.12 65.86
## 341 2016-02-15 1 0.12 65.98
## 342 2016-02-17 1 0.12 66.10
## 343 2016-02-21 1 0.12 66.22
## 344 2016-02-22 1 0.12 66.34
## 345 2016-02-24 1 0.12 66.46
## 346 2016-02-25 1 0.12 66.58
## 347 2016-02-26 1 0.12 66.70
## 348 2016-02-28 1 0.12 66.82
## 349 2016-02-29 1 0.12 66.94
## 350 2016-03-02 1 0.12 67.06
## 351 2016-03-03 1 0.12 67.18
## 352 2016-03-04 1 0.12 67.30
## 353 2016-03-05 1 0.12 67.42
## 354 2016-03-07 1 0.12 67.54
## 355 2016-03-08 1 0.12 67.66
## 356 2016-05-02 1 0.12 67.78
## 357 2016-05-03 1 0.12 67.90
## 358 2016-05-04 1 0.12 68.02
## 359 2016-05-05 1 0.12 68.14
## 360 2016-05-06 1 0.12 68.26
## 361 2016-05-07 1 0.12 68.38
## 362 2016-05-08 1 0.12 68.50
## 363 2016-05-09 1 0.12 68.62
## 364 2016-05-10 1 0.12 68.74
## 365 2016-05-11 1 0.12 68.86
## 366 2016-05-13 1 0.12 68.98
## 367 2016-05-14 1 0.12 69.10
## 368 2016-05-15 1 0.12 69.22
## 369 2016-05-16 1 0.12 69.34
## 370 2016-05-31 1 0.12 69.46
## 371 2016-06-22 1 0.12 69.58
## 372 2016-06-25 1 0.12 69.70
## 373 2016-06-26 1 0.12 69.82
## 374 2016-06-28 1 0.12 69.94
## 375 2016-06-29 1 0.12 70.06
## 376 2016-06-30 1 0.12 70.18
## 377 2016-07-04 1 0.12 70.30
## 378 2016-07-05 1 0.12 70.42
## 379 2016-07-06 1 0.12 70.54
## 380 2016-07-11 1 0.12 70.66
## 381 2016-07-24 1 0.12 70.78
## 382 2016-07-27 1 0.12 70.90
## 383 2016-08-05 1 0.12 71.02
## 384 2016-08-15 1 0.12 71.14
## 385 2016-08-17 1 0.12 71.26
## 386 2016-08-18 1 0.12 71.38
## 387 2016-08-22 1 0.12 71.50
## 388 2016-08-25 1 0.12 71.62
## 389 2016-08-28 1 0.12 71.74
## 390 2016-09-06 1 0.12 71.86
## 391 2016-09-09 1 0.12 71.98
## 392 2016-09-16 1 0.12 72.10
## 393 2016-09-19 1 0.12 72.22
## 394 2016-09-20 1 0.12 72.34
## 395 2016-09-23 1 0.12 72.46
## 396 2016-10-01 1 0.12 72.58
## 397 2016-10-12 1 0.12 72.70
## 398 2016-10-21 1 0.12 72.82
## 399 2016-10-22 1 0.12 72.94
## 400 2016-11-03 1 0.12 73.06
## 401 2016-11-07 1 0.12 73.18
## 402 2016-11-15 1 0.12 73.30
## 403 2016-11-16 1 0.12 73.42
## 404 2016-11-17 1 0.12 73.54
## 405 2016-11-22 1 0.12 73.66
## 406 2016-11-28 1 0.12 73.78
## 407 2016-11-30 1 0.12 73.90
## 408 2016-12-15 1 0.12 74.02
## 409 2016-12-22 1 0.12 74.14
## 410 2016-12-23 1 0.12 74.26
## 411 2016-12-25 1 0.12 74.38
## 412 2017-01-16 1 0.12 74.50
## 413 2017-01-21 1 0.12 74.62
## 414 2017-01-30 1 0.12 74.74
## 415 2017-02-04 1 0.12 74.86
## 416 2017-03-04 1 0.12 74.98
## 417 2017-03-06 1 0.12 75.10
## 418 2017-03-10 1 0.12 75.22
## 419 2017-03-18 1 0.12 75.34
## 420 2017-03-21 1 0.12 75.46
## 421 2017-03-22 1 0.12 75.58
## 422 2017-03-23 1 0.12 75.70
## 423 2017-03-24 1 0.12 75.82
## 424 2017-03-25 1 0.12 75.94
## 425 2017-03-26 1 0.12 76.06
## 426 2017-03-28 1 0.12 76.18
## 427 2017-04-01 1 0.12 76.30
## 428 2017-04-04 1 0.12 76.42
## 429 2017-04-05 1 0.12 76.54
## 430 2017-04-11 1 0.12 76.66
## 431 2017-04-12 1 0.12 76.78
## 432 2017-04-13 1 0.12 76.90
## 433 2017-04-22 1 0.12 77.02
## 434 2017-04-25 1 0.12 77.14
## 435 2017-04-26 1 0.12 77.26
## 436 2017-04-30 1 0.12 77.38
## 437 2017-05-06 1 0.12 77.50
## 438 2017-07-14 1 0.12 77.62
## 439 2017-07-25 1 0.12 77.74
## 440 2017-07-28 1 0.12 77.86
## 441 2017-08-05 1 0.12 77.98
## 442 2017-08-06 1 0.12 78.10
## 443 2017-08-23 1 0.12 78.22
## 444 2017-08-25 1 0.12 78.34
## 445 2017-08-26 1 0.12 78.46
## 446 2017-09-04 1 0.12 78.58
## 447 2017-09-11 1 0.12 78.70
## 448 2017-09-20 1 0.12 78.82
## 449 2017-09-21 1 0.12 78.94
## 450 2017-09-26 1 0.12 79.06
## 451 2017-10-02 1 0.12 79.18
## 452 2017-10-15 1 0.12 79.30
## 453 2017-10-16 1 0.12 79.42
## 454 2017-10-21 1 0.12 79.54
## 455 2017-10-23 1 0.12 79.66
## 456 2017-10-29 1 0.12 79.78
## 457 2017-10-30 1 0.12 79.90
## 458 2017-11-06 1 0.12 80.02
## 459 2017-11-12 1 0.12 80.14
## 460 2017-11-20 1 0.12 80.26
## 461 2017-11-23 1 0.12 80.38
## 462 2017-11-26 1 0.12 80.50
## 463 2017-12-04 1 0.12 80.62
## 464 2017-12-07 1 0.12 80.74
## 465 2017-12-10 1 0.12 80.86
## 466 2017-12-12 1 0.12 80.98
## 467 2017-12-21 1 0.12 81.10
## 468 2017-12-22 1 0.12 81.22
## 469 2017-12-23 1 0.12 81.34
## 470 2017-12-24 1 0.12 81.46
## 471 2017-12-25 1 0.12 81.58
## 472 2017-12-26 1 0.12 81.70
## 473 2017-12-27 1 0.12 81.82
## 474 2018-01-05 1 0.12 81.94
## 475 2018-01-10 1 0.12 82.06
## 476 2018-01-18 1 0.12 82.18
## 477 2018-01-28 1 0.12 82.30
## 478 2018-02-01 1 0.12 82.42
## 479 2018-02-09 1 0.12 82.54
## 480 2018-02-10 1 0.12 82.66
## 481 2018-02-11 1 0.12 82.78
## 482 2018-02-18 1 0.12 82.90
## 483 2018-02-21 1 0.12 83.02
## 484 2018-02-24 1 0.12 83.14
## 485 2018-02-28 1 0.12 83.26
## 486 2018-03-05 1 0.12 83.38
## 487 2018-03-08 1 0.12 83.50
## 488 2018-03-09 1 0.12 83.62
## 489 2018-03-10 1 0.12 83.74
## 490 2018-03-11 1 0.12 83.86
## 491 2018-03-12 1 0.12 83.98
## 492 2018-03-22 1 0.12 84.10
## 493 2018-03-24 1 0.12 84.22
## 494 2018-03-28 1 0.12 84.34
## 495 2018-03-31 1 0.12 84.46
## 496 2018-04-08 1 0.12 84.58
## 497 2018-04-09 1 0.12 84.70
## 498 2018-04-10 1 0.12 84.82
## 499 2018-04-11 1 0.12 84.94
## 500 2018-04-16 1 0.12 85.06
## 501 2018-04-19 1 0.12 85.18
## 502 2018-04-30 1 0.12 85.30
## 503 2018-05-04 1 0.12 85.42
## 504 2018-05-05 1 0.12 85.54
## 505 2018-05-15 1 0.12 85.66
## 506 2018-05-16 1 0.12 85.78
## 507 2018-05-17 1 0.12 85.90
## 508 2018-05-18 1 0.12 86.02
## 509 2018-05-22 1 0.12 86.14
## 510 2018-05-27 1 0.12 86.26
## 511 2018-06-08 1 0.12 86.38
## 512 2018-06-19 1 0.12 86.50
## 513 2018-06-25 1 0.12 86.62
## 514 2018-06-26 1 0.12 86.74
## 515 2018-07-01 1 0.12 86.86
## 516 2018-07-02 1 0.12 86.98
## 517 2018-07-16 1 0.12 87.10
## 518 2018-07-24 1 0.12 87.22
## 519 2018-07-26 1 0.12 87.34
## 520 2018-07-28 1 0.12 87.46
## 521 2018-08-05 1 0.12 87.58
## 522 2018-08-10 1 0.12 87.70
## 523 2018-08-11 1 0.12 87.82
## 524 2018-08-14 1 0.12 87.94
## 525 2018-08-15 1 0.12 88.06
## 526 2018-08-18 1 0.12 88.18
## 527 2018-08-28 1 0.12 88.30
## 528 2018-08-29 1 0.12 88.42
## 529 2018-08-30 1 0.12 88.54
## 530 2018-08-31 1 0.12 88.66
## 531 2018-09-01 1 0.12 88.78
## 532 2018-09-13 1 0.12 88.90
## 533 2018-09-14 1 0.12 89.02
## 534 2018-09-16 1 0.12 89.14
## 535 2018-09-30 1 0.12 89.26
## 536 2018-10-01 1 0.12 89.38
## 537 2018-10-02 1 0.12 89.50
## 538 2018-10-03 1 0.12 89.62
## 539 2018-10-10 1 0.12 89.74
## 540 2018-10-18 1 0.12 89.86
## 541 2018-10-19 1 0.12 89.98
## 542 2018-10-29 1 0.12 90.10
## 543 2018-11-03 1 0.12 90.22
## 544 2018-11-04 1 0.12 90.34
## 545 2018-11-05 1 0.12 90.46
## 546 2018-11-06 1 0.12 90.58
## 547 2018-11-21 1 0.12 90.70
## 548 2018-11-24 1 0.12 90.82
## 549 2018-12-01 1 0.12 90.94
## 550 2018-12-05 1 0.12 91.06
## 551 2018-12-13 1 0.12 91.18
## 552 2018-12-19 1 0.12 91.30
## 553 2018-12-23 1 0.12 91.42
## 554 2019-01-02 1 0.12 91.54
## 555 2019-01-04 1 0.12 91.66
## 556 2019-01-05 1 0.12 91.78
## 557 2019-01-06 1 0.12 91.90
## 558 2019-01-07 1 0.12 92.02
## 559 2019-01-08 1 0.12 92.14
## 560 2019-01-19 1 0.12 92.26
## 561 2019-02-02 1 0.12 92.38
## 562 2019-02-03 1 0.12 92.50
## 563 2019-02-04 1 0.12 92.62
## 564 2019-02-05 1 0.12 92.74
## 565 2019-02-06 1 0.12 92.86
## 566 2019-02-07 1 0.12 92.98
## 567 2019-02-08 1 0.12 93.10
## 568 2019-02-11 1 0.12 93.22
## 569 2019-03-02 1 0.12 93.34
## 570 2019-03-08 1 0.12 93.46
## 571 2019-03-22 1 0.12 93.58
## 572 2019-04-07 1 0.12 93.70
## 573 2019-04-12 1 0.12 93.82
## 574 2019-04-13 1 0.12 93.94
## 575 2019-04-14 1 0.12 94.06
## 576 2019-04-15 1 0.12 94.18
## 577 2019-04-20 1 0.12 94.30
## 578 2019-04-25 1 0.12 94.42
## 579 2019-04-28 1 0.12 94.54
## 580 2019-05-11 1 0.12 94.66
## 581 2019-05-12 1 0.12 94.78
## 582 2019-05-19 1 0.12 94.90
## 583 2019-05-25 1 0.12 95.02
## 584 2019-06-09 1 0.12 95.14
## 585 2019-06-16 1 0.12 95.26
## 586 2019-06-17 1 0.12 95.38
## 587 2019-06-18 1 0.12 95.50
## 588 2019-06-19 1 0.12 95.62
## 589 2019-06-20 1 0.12 95.74
## 590 2019-06-29 1 0.12 95.86
## 591 2019-07-04 1 0.12 95.98
## 592 2019-07-17 1 0.12 96.10
## 593 2019-07-19 1 0.12 96.22
## 594 2019-07-20 1 0.12 96.34
## 595 2019-07-21 1 0.12 96.46
## 596 2019-07-22 1 0.12 96.58
## 597 2019-07-23 1 0.12 96.70
## 598 2019-07-24 1 0.12 96.82
## 599 2019-07-29 1 0.12 96.94
## 600 2019-08-20 1 0.12 97.06
## 601 2019-09-18 1 0.12 97.18
## 602 2019-09-20 1 0.12 97.30
## 603 2019-10-22 1 0.12 97.42
## 604 2019-11-18 1 0.12 97.54
## 605 2019-11-19 1 0.12 97.66
## 606 2019-11-20 1 0.12 97.78
## 607 2019-11-21 1 0.12 97.90
## 608 2019-11-22 1 0.12 98.02
## 609 2019-11-23 1 0.12 98.14
## 610 2019-11-27 1 0.12 98.26
## 611 2019-12-16 1 0.12 98.38
## 612 2019-12-19 1 0.12 98.50
## 613 2019-12-20 1 0.12 98.62
## 614 2020-01-10 1 0.12 98.74
## 615 2020-01-21 1 0.12 98.86
## 616 2020-01-26 1 0.12 98.98
## 617 2020-01-27 1 0.12 99.10
## 618 2020-01-28 1 0.12 99.22
## 619 2020-01-29 1 0.12 99.34
## 620 2020-02-10 1 0.12 99.46
## 621 2020-02-22 1 0.12 99.58
## 622 2020-02-26 1 0.12 99.70
## 623 2020-03-08 1 0.12 99.82
## 624 2020-03-17 1 0.12 99.94
## 625 2020-03-25 1 0.12 100.06
## 626 2020-04-09 1 0.12 100.18
## 627 2020-04-19 1 0.12 100.30
## 628 2020-04-24 1 0.12 100.42
## 629 2020-05-09 1 0.12 100.54
## 630 2020-05-12 1 0.12 100.66
## 631 2020-05-13 1 0.12 100.78
## 632 2020-05-14 1 0.12 100.90
## 633 2020-05-15 1 0.12 101.02
## 634 2020-05-26 1 0.12 101.14
## 635 2020-06-10 1 0.12 101.26
## 636 2020-06-11 1 0.12 100.00
## symptom_id frequency percentage cumulative_perc
## 1 0239f552-c6b3-42ff-8005-0d45ea1f8962 308 36.02 36.02
## 2 bdd8fb86-4cbf-4832-b975-742cd3109215 182 21.29 57.31
## 3 54bb1081-2244-41cf-baf7-47beb31ea4d2 100 11.70 69.01
## 4 86a80065-96fb-4069-ad49-2c100b866914 77 9.01 78.02
## 5 c8c60842-b805-41ba-b7b9-d772e39ff3b5 66 7.72 85.74
## 6 a2c90fad-596d-4cb9-a4c0-a840af41e574 41 4.80 90.54
## 7 <NA> 18 2.11 92.65
## 8 a2fcc57c-1f75-4411-877b-b82cf6d9ed0b 17 1.99 94.64
## 9 0fa4d6da-91de-4504-a578-4bcc75d525c8 14 1.64 96.28
## 10 fc56b772-6b14-45f3-aaec-76efe14f401d 7 0.82 97.10
## 11 2a50ec7a-7d5c-462a-8004-076e552e84ec 4 0.47 97.57
## 12 cc0cc290-cbb2-464f-8174-9a5bce0fe639 4 0.47 98.04
## 13 0 1 0.12 98.16
## 14 158f47bc-b2c5-463e-bffe-59db9423fe00 1 0.12 98.28
## 15 33255143-8303-4453-b22b-8acc17be5089 1 0.12 98.40
## 16 36e174ac-8a13-4ab0-9e96-6fbd6f890fa5 1 0.12 98.52
## 17 3f06bd87-7111-4b1e-b0e9-7affac3702c6 1 0.12 98.64
## 18 4c4f63de-cc59-4249-95ed-878129f2178e 1 0.12 98.76
## 19 56df8fa6-47e0-45f2-93d0-6ea612609007 1 0.12 98.88
## 20 67d1d557-6482-4465-a1cb-a5d9374ed188 1 0.12 99.00
## 21 6eef6051-d2b4-4812-af17-7e5f4d3193d7 1 0.12 99.12
## 22 77de7f81-105e-4ddd-9828-667741b07dab 1 0.12 99.24
## 23 8110c0d4-4cf9-44e1-b1e4-2945ed35a6d4 1 0.12 99.36
## 24 8a63e2b4-61d7-43af-904d-9eff6e93392a 1 0.12 99.48
## 25 9c86dea7-6a8e-401f-9692-90e6ee0d23d7 1 0.12 99.60
## 26 b5a9080d-e2dc-42bc-b85b-a9f9cd9d5a59 1 0.12 99.72
## 27 ea62cb53-18f0-4f27-a236-8bd695926a8b 1 0.12 99.84
## 28 f93bc06f-1489-410b-a02b-d0dd05d6fce5 1 0.12 99.96
## 29 fd03ae70-ad11-4b31-ada8-f33ada07f92f 1 0.12 100.00
## name.x frequency percentage cumulative_perc
## 1 Relación sexual 308 36.02 36.02
## 2 Menstruación 182 21.29 57.31
## 3 Peso 100 11.70 69.01
## 4 Test de ovulación 77 9.01 78.02
## 5 Citas médicas 66 7.72 85.74
## 6 Medicamentos 41 4.80 90.54
## 7 <NA> 19 2.22 92.76
## 8 Temperatura 17 1.99 94.75
## 9 Prueba del embarazo 14 1.64 96.39
## 10 Dolores de cabeza 7 0.82 97.21
## 11 Náuseas / vomitos 4 0.47 97.68
## 12 Pechos sensibles 4 0.47 98.15
## 13 Acné cíclico 1 0.12 98.27
## 14 Calambres abdominales 1 0.12 98.39
## 15 Cervical apertura 1 0.12 98.51
## 16 Día del ciclo 1 0.12 98.63
## 17 Día del mes 1 0.12 98.75
## 18 Dolor muscular / articular 1 0.12 98.87
## 19 Estado de ánimo 1 0.12 98.99
## 20 Firmeza cervical 1 0.12 99.11
## 21 Hinchazón 1 0.12 99.23
## 22 Insomnio 1 0.12 99.35
## 23 Manchado / Sangrado 1 0.12 99.47
## 24 Menstruación prevista 1 0.12 99.59
## 25 Moco cervical 1 0.12 99.71
## 26 Notas 1 0.12 99.83
## 27 Ovulación prevista 1 0.12 99.95
## 28 Posición cervical 1 0.12 100.00
## num.x frequency percentage cumulative_perc
## 1 0 308 36.02 36.02
## 2 -9999 182 21.29 57.31
## 3 -9986 100 11.70 69.01
## 4 12 77 9.01 78.02
## 5 14 66 7.72 85.74
## 6 13 41 4.80 90.54
## 7 <NA> 19 2.22 92.76
## 8 -9987 17 1.99 94.75
## 9 11 14 1.64 96.39
## 10 2 7 0.82 97.21
## 11 3 4 0.47 97.68
## 12 7 4 0.47 98.15
## 13 -8998 1 0.12 98.27
## 14 -8999 1 0.12 98.39
## 15 -9994 1 0.12 98.51
## 16 -9995 1 0.12 98.63
## 17 -9998 1 0.12 98.75
## 18 1 1 0.12 98.87
## 19 10 1 0.12 98.99
## 20 15 1 0.12 99.11
## 21 16 1 0.12 99.23
## 22 17 1 0.12 99.35
## 23 18 1 0.12 99.47
## 24 4 1 0.12 99.59
## 25 5 1 0.12 99.71
## 26 6 1 0.12 99.83
## 27 8 1 0.12 99.95
## 28 9 1 0.12 100.00
## mode frequency percentage cumulative_perc
## 1 0 719 84.09 84.09
## 2 1 117 13.68 97.77
## 3 <NA> 19 2.22 100.00
## type frequency percentage cumulative_perc
## 1 1 408 47.72 47.72
## 2 4 308 36.02 83.74
## 3 2 78 9.12 92.86
## 4 <NA> 19 2.22 95.08
## 5 ° 17 1.99 97.07
## 6 9 14 1.64 98.71
## 7 6 8 0.94 99.65
## 8 5 1 0.12 99.77
## 9 7 1 0.12 99.89
## 10 8 1 0.12 100.00
## layout frequency percentage cumulative_perc
## 1 2 308 36.02 36.02
## 2 0 191 22.34 58.36
## 3 3 183 21.40 79.76
## 4 7 66 7.72 87.48
## 5 5 41 4.80 92.28
## 6 1 26 3.04 95.32
## 7 <NA> 19 2.22 97.54
## 8 2a50ec7a-7d5c-462a-8004-076e552e84ec 4 0.47 98.01
## 9 cc0cc290-cbb2-464f-8174-9a5bce0fe639 4 0.47 98.48
## 10 -1 3 0.35 98.83
## 11 33255143-8303-4453-b22b-8acc17be5089 1 0.12 98.95
## 12 36e174ac-8a13-4ab0-9e96-6fbd6f890fa5 1 0.12 99.07
## 13 4c4f63de-cc59-4249-95ed-878129f2178e 1 0.12 99.19
## 14 56df8fa6-47e0-45f2-93d0-6ea612609007 1 0.12 99.31
## 15 67d1d557-6482-4465-a1cb-a5d9374ed188 1 0.12 99.43
## 16 8 1 0.12 99.55
## 17 8a63e2b4-61d7-43af-904d-9eff6e93392a 1 0.12 99.67
## 18 9c86dea7-6a8e-401f-9692-90e6ee0d23d7 1 0.12 99.79
## 19 ea62cb53-18f0-4f27-a236-8bd695926a8b 1 0.12 99.91
## 20 fd03ae70-ad11-4b31-ada8-f33ada07f92f 1 0.12 100.00
## chart frequency percentage cumulative_perc
## 1 0 432 50.53 50.53
## 2 1 183 21.40 71.93
## 3 54bb1081-2244-41cf-baf7-47beb31ea4d2 100 11.70 83.63
## 4 86a80065-96fb-4069-ad49-2c100b866914 77 9.01 92.64
## 5 <NA> 19 2.22 94.86
## 6 0fa4d6da-91de-4504-a578-4bcc75d525c8 14 1.64 96.50
## 7 fc56b772-6b14-45f3-aaec-76efe14f401d 7 0.82 97.32
## 8 Náuseas / vomitos 4 0.47 97.79
## 9 Pechos sensibles 4 0.47 98.26
## 10 -1 2 0.23 98.49
## 11 158f47bc-b2c5-463e-bffe-59db9423fe00 1 0.12 98.61
## 12 6eef6051-d2b4-4812-af17-7e5f4d3193d7 1 0.12 98.73
## 13 77de7f81-105e-4ddd-9828-667741b07dab 1 0.12 98.85
## 14 Acné cíclico 1 0.12 98.97
## 15 Calambres abdominales 1 0.12 99.09
## 16 Cervical apertura 1 0.12 99.21
## 17 Dolor muscular / articular 1 0.12 99.33
## 18 f93bc06f-1489-410b-a02b-d0dd05d6fce5 1 0.12 99.45
## 19 Firmeza cervical 1 0.12 99.57
## 20 Hinchazón 1 0.12 99.69
## 21 Insomnio 1 0.12 99.81
## 22 Manchado / Sangrado 1 0.12 99.93
## 23 Posición cervical 1 0.12 100.00
## category frequency percentage cumulative_perc
## 1 0239f552-c6b3-42ff-8005-0d45ea1f8962 308 36.02 36.02
## 2 0 182 21.29 57.31
## 3 Peso 100 11.70 69.01
## 4 Test de ovulación 77 9.01 78.02
## 5 c8c60842-b805-41ba-b7b9-d772e39ff3b5 66 7.72 85.74
## 6 a2c90fad-596d-4cb9-a4c0-a840af41e574 41 4.80 90.54
## 7 <NA> 19 2.22 92.76
## 8 a2fcc57c-1f75-4411-877b-b82cf6d9ed0b 17 1.99 94.75
## 9 Prueba del embarazo 14 1.64 96.39
## 10 Dolores de cabeza 7 0.82 97.21
## 11 3 4 0.47 97.68
## 12 7 4 0.47 98.15
## 13 10 1 0.12 98.27
## 14 16 1 0.12 98.39
## 15 17 1 0.12 98.51
## 16 18 1 0.12 98.63
## 17 3f06bd87-7111-4b1e-b0e9-7affac3702c6 1 0.12 98.75
## 18 4 1 0.12 98.87
## 19 5 1 0.12 98.99
## 20 6 1 0.12 99.11
## 21 8 1 0.12 99.23
## 22 8110c0d4-4cf9-44e1-b1e4-2945ed35a6d4 1 0.12 99.35
## 23 9 1 0.12 99.47
## 24 b5a9080d-e2dc-42bc-b85b-a9f9cd9d5a59 1 0.12 99.59
## 25 Día del ciclo 1 0.12 99.71
## 26 Menstruación prevista 1 0.12 99.83
## 27 Moco cervical 1 0.12 99.95
## 28 Ovulación prevista 1 0.12 100.00
##
## id.x frequency percentage cumulative_perc
## 1 <NA> 738 86.32 86.32
## 2 009f6884-11f7-4d4e-8b1b-05c809f9be12 1 0.12 86.44
## 3 0141878b-80d4-44dc-8964-46bd870e39db 1 0.12 86.56
## 4 0292b053-4f55-4f77-97dc-91f7a6dc2341 1 0.12 86.68
## 5 03fbc39f-b17f-41cd-8d09-c1fb7e0f5951 1 0.12 86.80
## 6 0b992fff-4835-41a7-8a20-0813ea811087 1 0.12 86.92
## 7 0f1cf32d-49a6-44ab-99a1-9919dc0816aa 1 0.12 87.04
## 8 0f5b7e94-696e-42d8-9008-9d6718bada3d 1 0.12 87.16
## 9 114cd32d-dc78-4c46-9e84-7a931204def5 1 0.12 87.28
## 10 1286a1a9-7cb3-44eb-a863-11084c689914 1 0.12 87.40
## 11 175d95b1-33ee-47ec-a825-e1c40724c490 1 0.12 87.52
## 12 1aa242f5-feeb-416f-b509-9b7c4caf04c0 1 0.12 87.64
## 13 1c87bfc6-d5e2-42dd-ac64-1de87962f370 1 0.12 87.76
## 14 1fa638c7-d392-40a0-9944-8d13117147e1 1 0.12 87.88
## 15 2019a077-50af-4fe9-ab4e-01a207885f9e 1 0.12 88.00
## 16 24f9d316-2eec-4d01-bab3-131cb6d148f6 1 0.12 88.12
## 17 252ca8f2-b032-4e5f-908c-858e47a827fb 1 0.12 88.24
## 18 2975d8a0-32b7-4546-8adb-a6553098fa46 1 0.12 88.36
## 19 2ace358a-f14d-4fe9-9344-c3b437ad5b73 1 0.12 88.48
## 20 2c47e46d-1d11-4c62-b09e-de8a095a9933 1 0.12 88.60
## 21 2c5bcc0b-7c15-488e-a6ae-3a8fe615ff1c 1 0.12 88.72
## 22 2d7af972-bd43-4d00-b4cc-690477165908 1 0.12 88.84
## 23 31baec56-f915-4dee-8c99-b9ca03b20c45 1 0.12 88.96
## 24 32780a6d-e81c-443f-8ce2-290f25685538 1 0.12 89.08
## 25 38f7e1c6-e140-44d0-af64-b6ea0989657a 1 0.12 89.20
## 26 479fba03-0018-4cac-98c0-3aaf49a55bb9 1 0.12 89.32
## 27 49a10b71-196d-4602-af71-f8a8694e61d7 1 0.12 89.44
## 28 49c4de80-a5c4-4eac-af9a-b52c28970ec7 1 0.12 89.56
## 29 4bee07bc-4f57-41f5-9025-4ccd05ee0b8a 1 0.12 89.68
## 30 4e5a9eba-4f7d-4999-839a-ba09225109ff 1 0.12 89.80
## 31 4ef87e7e-b2bb-48c9-ba81-a978d2db77f2 1 0.12 89.92
## 32 505852e5-b705-42b7-a1cf-e77bf2674a89 1 0.12 90.04
## 33 52a7a27b-a913-4e21-9fe4-e252db7641ca 1 0.12 90.16
## 34 5ccc87ec-a3f7-45ff-ae95-b50bc1d708cf 1 0.12 90.28
## 35 5dea783d-7dcf-4cc8-afdc-63b0e9976f0a 1 0.12 90.40
## 36 5fd8bb7c-6243-4e67-937a-47adf713ca01 1 0.12 90.52
## 37 61619736-8fa8-4590-a6a6-ccecb5ec7f5c 1 0.12 90.64
## 38 616f76c7-1cdb-47f5-adfb-cddd11c54225 1 0.12 90.76
## 39 61d44209-e968-4af3-b9a6-0b24e4d2825c 1 0.12 90.88
## 40 61ea8af3-df8b-490d-a740-54d0c24a2fd3 1 0.12 91.00
## 41 64ccd022-4d41-4025-99d1-00d096a32b51 1 0.12 91.12
## 42 67c7b2dd-2a65-4a2d-8bd3-94cf561a1dde 1 0.12 91.24
## 43 695bdb63-7c1d-472f-b6d9-0c6b075b088a 1 0.12 91.36
## 44 6a538482-2223-40de-87fa-f711e52bd7fb 1 0.12 91.48
## 45 6fb6bb3f-5648-461b-bba8-f36800f9593b 1 0.12 91.60
## 46 6fbaf868-1e2a-49c0-8f39-96122403fb45 1 0.12 91.72
## 47 707fe1df-40c3-4703-a3fa-55d715b2655f 1 0.12 91.84
## 48 71adbecb-14d0-4135-b147-ee2fbbbbe54f 1 0.12 91.96
## 49 731c1a84-29a2-4c18-9006-90347d26a16a 1 0.12 92.08
## 50 76bbf481-beb8-42f1-84a3-12b399e310fc 1 0.12 92.20
## 51 79179bfb-c97d-41da-8942-1d2f9e296428 1 0.12 92.32
## 52 7d4e8ef7-6673-4729-9452-3ef604cac137 1 0.12 92.44
## 53 7d732639-d8a8-403b-b54f-de901c5e2333 1 0.12 92.56
## 54 7e6424bf-fccc-4cd7-98a7-65e274cd8dda 1 0.12 92.68
## 55 8018007d-733a-4f49-acb8-57ebe5567583 1 0.12 92.80
## 56 82477496-5650-4710-88ce-34cf7d98c866 1 0.12 92.92
## 57 867d49c8-4938-45f7-827d-066ff47381f4 1 0.12 93.04
## 58 86e58d8a-ec12-44bd-907e-b654271d243e 1 0.12 93.16
## 59 886f66d0-ac4e-464c-a986-53640cb4f617 1 0.12 93.28
## 60 8985d537-49a1-4b1d-99b0-310aeeef22f5 1 0.12 93.40
## 61 89c72186-96a0-4f24-bfc9-30a5a1791aaf 1 0.12 93.52
## 62 8b66908e-c63e-4c49-be51-06309b6b839e 1 0.12 93.64
## 63 8c8f6538-059e-4663-af2c-c3580d4f453e 1 0.12 93.76
## 64 8c9bf8e1-d8df-46c8-a08a-0ecc7fd82a1e 1 0.12 93.88
## 65 8c9f069d-d6a3-4d2f-b489-bb029e42a5a4 1 0.12 94.00
## 66 90acdbe6-9468-4dc7-ad91-ce03d217bdf7 1 0.12 94.12
## 67 92f68a3e-1b0c-4b53-9e1e-2a625218e741 1 0.12 94.24
## 68 93de1a0d-d4f2-4aa6-8b99-e34ce48e789a 1 0.12 94.36
## 69 93e8189f-8c3f-4c21-a969-83b931227d67 1 0.12 94.48
## 70 95bd3708-ef95-42f3-94dc-c1921e9f8fcf 1 0.12 94.60
## 71 95de4f80-70c2-4a31-84b3-c5beb5a0207a 1 0.12 94.72
## 72 9c351a15-006b-400f-a847-fbe54fd4d7d1 1 0.12 94.84
## 73 9cd967b8-5da6-413a-94c2-e02b7195d8a8 1 0.12 94.96
## 74 a238bb9e-b510-44a0-a435-3417831c00d4 1 0.12 95.08
## 75 a290cb92-dd32-459f-a6c0-8e36d658abfe 1 0.12 95.20
## 76 a5923237-e41c-4a5f-ab8a-91b105433a4e 1 0.12 95.32
## 77 a676ae84-3764-49af-88b4-7994c75d7772 1 0.12 95.44
## 78 a9327ee7-d08e-405a-bc60-6100d983695d 1 0.12 95.56
## 79 ac3ff79d-eb22-4552-83fd-f9a9d211ec8f 1 0.12 95.68
## 80 acca2320-2caf-4473-9817-e98f85acafea 1 0.12 95.80
## 81 afaff20d-dacd-4b0e-a1f1-f8d81e6ab9d3 1 0.12 95.92
## 82 b30e211d-f831-45bb-a0b5-0d1c4c2b64c2 1 0.12 96.04
## 83 b6616594-7898-4edb-9e11-5383e02d2bbe 1 0.12 96.16
## 84 b6636aec-d8fa-4c33-89ed-f50ee017ec67 1 0.12 96.28
## 85 b9580051-c662-4584-a8c5-bfd3b9ec3c94 1 0.12 96.40
## 86 b9992780-24d9-4198-93b1-5dd615ec0b6e 1 0.12 96.52
## 87 b9f07c5c-56e5-41d6-af04-ff8523c03358 1 0.12 96.64
## 88 bc2854a7-1d88-4bdc-9394-f4e7bd225696 1 0.12 96.76
## 89 bd8c4384-8713-4633-bcfa-7407ea688c46 1 0.12 96.88
## 90 bfbc2f59-1896-4f13-aa11-6c3c5a4e11d2 1 0.12 97.00
## 91 c068442e-a212-40c3-b027-6005774be399 1 0.12 97.12
## 92 c1136d00-97c6-4853-805a-9108d2967a04 1 0.12 97.24
## 93 c2ce232b-e6a7-4d2c-b865-b0f3c3dd0d6f 1 0.12 97.36
## 94 c92d7121-58bb-4471-92df-bdfa7a51ddd5 1 0.12 97.48
## 95 cd70e49b-a972-48d5-9cb7-b6265e812d53 1 0.12 97.60
## 96 cd7a8308-fc9f-489a-bf7f-2bd310962b10 1 0.12 97.72
## 97 d01a4b09-5f80-4ac7-9442-3c85844bb0f3 1 0.12 97.84
## 98 d41eae90-6db9-47f4-9043-f5fa9cc864cd 1 0.12 97.96
## 99 d6a225a2-83ed-447b-82d5-99dfa63591cd 1 0.12 98.08
## 100 d7c08024-5218-4d32-ba67-16e7a1ce2114 1 0.12 98.20
## 101 d9b7a748-719f-4dcc-9062-03f5f09384b7 1 0.12 98.32
## 102 dde3c5cf-ce90-4e8e-af2a-fb6fe0a7b755 1 0.12 98.44
## 103 df080b87-0ddd-404a-ae22-a237fcac008e 1 0.12 98.56
## 104 df5cd6fc-b2e7-4d23-b75d-5c000abcd1af 1 0.12 98.68
## 105 e00e03b3-9708-475c-9cd6-52cefd78a32d 1 0.12 98.80
## 106 e05f5fce-d669-49c1-8d87-1d9d08331d92 1 0.12 98.92
## 107 e1458981-6963-4d33-8972-4cb1fcededc5 1 0.12 99.04
## 108 e3e86bc0-ab8e-4be5-81c1-c90a0b3d0798 1 0.12 99.16
## 109 e4931a3a-fbc9-4ce9-928c-31c987026495 1 0.12 99.28
## 110 e4aea38a-d5af-4f9b-b09b-707d0da39e05 1 0.12 99.40
## 111 e99b0d6e-6925-4744-8c55-8ff4c31b48c2 1 0.12 99.52
## 112 ec3f4bce-65b0-4787-8901-d733baf1f8d6 1 0.12 99.64
## 113 ee17a51b-3549-442b-8729-6c48316262ee 1 0.12 99.76
## 114 f15a167e-5390-403f-a5c2-adb996ddda16 1 0.12 99.88
## 115 f573ddc8-7ca7-41ab-989b-508b2c77e5da 1 0.12 100.00
## 116 f5b93dce-1016-4acf-bd0b-c602f9cc54d1 1 0.12 100.12
## 117 f775a24e-e538-4808-b96e-29960b8cc32a 1 0.12 100.24
## 118 fd2ed856-ce1a-48ef-b2aa-8f117fbe4f5a 1 0.12 100.00
##
## datetime frequency percentage cumulative_perc
## 1 <NA> 738 86.32 86.32
## 2 2013-06-04T09:50:00 1 0.12 86.44
## 3 2013-06-09T10:02:00 1 0.12 86.56
## 4 2013-06-10T06:50:00 1 0.12 86.68
## 5 2013-06-14T09:15:00 1 0.12 86.80
## 6 2013-06-27T09:07:00 1 0.12 86.92
## 7 2013-07-24T09:51:00 1 0.12 87.04
## 8 2013-07-31T10:32:00 1 0.12 87.16
## 9 2013-08-02T14:30:00 1 0.12 87.28
## 10 2013-08-19T09:06:00 1 0.12 87.40
## 11 2013-08-23T10:42:00 1 0.12 87.52
## 12 2013-08-24T08:23:00 1 0.12 87.64
## 13 2013-09-05T07:19:00 1 0.12 87.76
## 14 2013-09-09T08:40:00 1 0.12 87.88
## 15 2013-09-10T08:27:00 1 0.12 88.00
## 16 2013-09-15T10:30:00 1 0.12 88.12
## 17 2014-09-30T07:10:00 1 0.12 88.24
## 18 2014-10-01T06:02:00 1 0.12 88.36
## 19 2014-10-02T04:56:00 1 0.12 88.48
## 20 2014-10-03T06:23:00 1 0.12 88.60
## 21 2014-10-10T07:07:00 1 0.12 88.72
## 22 2014-10-13T06:11:00 1 0.12 88.84
## 23 2014-10-20T06:02:00 1 0.12 88.96
## 24 2014-10-22T06:57:00 1 0.12 89.08
## 25 2014-10-23T05:30:00 1 0.12 89.20
## 26 2014-10-24T06:10:00 1 0.12 89.32
## 27 2014-10-25T09:18:00 1 0.12 89.44
## 28 2014-10-27T06:36:00 1 0.12 89.56
## 29 2014-10-28T06:17:00 1 0.12 89.68
## 30 2014-10-29T05:42:00 1 0.12 89.80
## 31 2014-10-30T06:27:00 1 0.12 89.92
## 32 2014-10-31T06:03:00 1 0.12 90.04
## 33 2014-11-01T07:48:00 1 0.12 90.16
## 34 2014-11-26T06:18:00 1 0.12 90.28
## 35 2015-01-15T06:09:00 1 0.12 90.40
## 36 2015-02-03T06:03:00 1 0.12 90.52
## 37 2015-03-10T07:11:00 1 0.12 90.64
## 38 2015-03-30T20:44:00 1 0.12 90.76
## 39 2015-09-30T06:14:00 1 0.12 90.88
## 40 2015-10-14T06:12:00 1 0.12 91.00
## 41 2015-11-02T06:08:00 1 0.12 91.12
## 42 2015-11-03T06:09:00 1 0.12 91.24
## 43 2015-11-10T06:09:00 1 0.12 91.36
## 44 2015-11-11T06:09:00 1 0.12 91.48
## 45 2015-11-12T06:15:00 1 0.12 91.60
## 46 2015-11-16T06:01:00 1 0.12 91.72
## 47 2015-11-17T06:50:00 1 0.12 91.84
## 48 2015-11-26T06:14:00 1 0.12 91.96
## 49 2015-12-06T08:23:00 1 0.12 92.08
## 50 2015-12-07T06:17:00 1 0.12 92.20
## 51 2015-12-08T11:03:00 1 0.12 92.32
## 52 2015-12-20T10:45:00 1 0.12 92.44
## 53 2016-01-11T06:14:00 1 0.12 92.56
## 54 2016-01-12T06:12:00 1 0.12 92.68
## 55 2016-01-14T06:26:00 1 0.12 92.80
## 56 2016-01-15T06:47:00 1 0.12 92.92
## 57 2016-01-19T06:19:00 1 0.12 93.04
## 58 2016-01-21T06:10:00 1 0.12 93.16
## 59 2016-01-26T07:09:00 1 0.12 93.28
## 60 2016-01-28T06:18:00 1 0.12 93.40
## 61 2016-02-17T06:11:00 1 0.12 93.52
## 62 2016-03-01T06:12:00 1 0.12 93.64
## 63 2016-03-04T06:17:00 1 0.12 93.76
## 64 2016-03-07T06:11:00 1 0.12 93.88
## 65 2016-03-09T06:18:00 1 0.12 94.00
## 66 2016-06-20T07:17:00 1 0.12 94.12
## 67 2016-06-21T06:29:00 1 0.12 94.24
## 68 2016-06-30T10:09:00 1 0.12 94.36
## 69 2016-07-01T10:42:00 1 0.12 94.48
## 70 2016-07-05T07:18:00 1 0.12 94.60
## 71 2016-07-07T08:09:00 1 0.12 94.72
## 72 2016-07-15T08:44:00 1 0.12 94.84
## 73 2016-07-21T06:14:00 1 0.12 94.96
## 74 2016-07-26T07:11:00 1 0.12 95.08
## 75 2016-08-05T07:12:00 1 0.12 95.20
## 76 2016-08-09T07:14:00 1 0.12 95.32
## 77 2016-08-15T08:48:00 1 0.12 95.44
## 78 2016-08-30T08:57:00 1 0.12 95.56
## 79 2016-09-12T06:26:00 1 0.12 95.68
## 80 2016-09-13T06:35:00 1 0.12 95.80
## 81 2016-09-16T06:11:00 1 0.12 95.92
## 82 2016-09-29T06:20:00 1 0.12 96.04
## 83 2016-09-30T06:17:00 1 0.12 96.16
## 84 2016-10-04T06:10:00 1 0.12 96.28
## 85 2016-10-14T07:14:00 1 0.12 96.40
## 86 2016-10-19T06:20:00 1 0.12 96.52
## 87 2016-10-21T06:57:00 1 0.12 96.64
## 88 2016-11-03T07:14:00 1 0.12 96.76
## 89 2016-11-14T07:14:00 1 0.12 96.88
## 90 2017-04-12T10:00:00 1 0.12 97.00
## 91 2017-07-19T07:25:00 1 0.12 97.12
## 92 2017-07-20T08:01:00 1 0.12 97.24
## 93 2017-07-21T12:35:00 1 0.12 97.36
## 94 2017-07-22T12:35:00 1 0.12 97.48
## 95 2017-07-25T09:59:00 1 0.12 97.60
## 96 2017-07-27T07:38:00 1 0.12 97.72
## 97 2017-08-14T16:05:00 1 0.12 97.84
## 98 2017-08-21T08:19:00 1 0.12 97.96
## 99 2017-08-24T10:32:00 1 0.12 98.08
## 100 2017-08-25T09:33:00 1 0.12 98.20
## 101 2017-09-10T08:14:00 1 0.12 98.32
## 102 2017-09-11T07:45:00 1 0.12 98.44
## 103 2017-09-19T11:57:00 1 0.12 98.56
## 104 2017-09-21T07:19:00 1 0.12 98.68
## 105 2017-09-26T08:15:00 1 0.12 98.80
## 106 2017-10-02T07:17:00 1 0.12 98.92
## 107 2017-10-16T08:20:00 1 0.12 99.04
## 108 2017-10-29T09:09:00 1 0.12 99.16
## 109 2017-11-23T08:50:00 1 0.12 99.28
## 110 2017-11-28T21:15:00 1 0.12 99.40
## 111 2017-12-07T08:53:00 1 0.12 99.52
## 112 2017-12-11T07:19:00 1 0.12 99.64
## 113 2017-12-19T09:59:00 1 0.12 99.76
## 114 2018-01-05T07:35:00 1 0.12 99.88
## 115 2018-07-04T11:50:00 1 0.12 100.00
## 116 2018-08-08T07:18:00 1 0.12 100.12
## 117 2018-11-24T09:43:00 1 0.12 100.24
## 118 2019-01-05T09:47:00 1 0.12 100.00
## value frequency percentage cumulative_perc
## 1 <NA> 738 86.32 86.32
## 2 70000 7 0.82 87.14
## 3 36700 5 0.58 87.72
## 4 36800 5 0.58 88.30
## 5 68700 5 0.58 88.88
## 6 70100 5 0.58 89.46
## 7 69500 4 0.47 89.93
## 8 69900 4 0.47 90.40
## 9 36600 3 0.35 90.75
## 10 68800 3 0.35 91.10
## 11 69200 3 0.35 91.45
## 12 70700 3 0.35 91.80
## 13 71100 3 0.35 92.15
## 14 73000 3 0.35 92.50
## 15 66400 2 0.23 92.73
## 16 66700 2 0.23 92.96
## 17 66800 2 0.23 93.19
## 18 66900 2 0.23 93.42
## 19 67700 2 0.23 93.65
## 20 68200 2 0.23 93.88
## 21 68900 2 0.23 94.11
## 22 69400 2 0.23 94.34
## 23 69700 2 0.23 94.57
## 24 70400 2 0.23 94.80
## 25 70600 2 0.23 95.03
## 26 70900 2 0.23 95.26
## 27 71200 2 0.23 95.49
## 28 73300 2 0.23 95.72
## 29 79900 2 0.23 95.95
## 30 36900 1 0.12 96.07
## 31 37000 1 0.12 96.19
## 32 37100 1 0.12 96.31
## 33 37300 1 0.12 96.43
## 34 66600 1 0.12 96.55
## 35 67000 1 0.12 96.67
## 36 67200 1 0.12 96.79
## 37 68000 1 0.12 96.91
## 38 68100 1 0.12 97.03
## 39 68300 1 0.12 97.15
## 40 68500 1 0.12 97.27
## 41 69000 1 0.12 97.39
## 42 69100 1 0.12 97.51
## 43 69300 1 0.12 97.63
## 44 69800 1 0.12 97.75
## 45 70200 1 0.12 97.87
## 46 70300 1 0.12 97.99
## 47 70500 1 0.12 98.11
## 48 71400 1 0.12 98.23
## 49 71700 1 0.12 98.35
## 50 72100 1 0.12 98.47
## 51 72400 1 0.12 98.59
## 52 72500 1 0.12 98.71
## 53 72900 1 0.12 98.83
## 54 73600 1 0.12 98.95
## 55 74100 1 0.12 99.07
## 56 75200 1 0.12 99.19
## 57 75300 1 0.12 99.31
## 58 76500 1 0.12 99.43
## 59 77600 1 0.12 99.55
## 60 78200 1 0.12 99.67
## 61 78300 1 0.12 99.79
## 62 78500 1 0.12 99.91
## 63 79000 1 0.12 100.00
## value_id frequency percentage cumulative_perc
## 1 b3128bcd-b057-4de1-b776-147dc6edb677 275 32.16 32.16
## 2 <NA> 150 17.54 49.70
## 3 44d8b929-c043-4e39-baf4-63dd5d04fe38 85 9.94 59.64
## 4 7442f43e-9551-4a4c-b2d5-ab73d180504a 74 8.65 68.29
## 5 23d86cea-d536-4343-ab59-a1bb86aec86e 68 7.95 76.24
## 6 cc56c587-77d5-489e-a650-f7814c6f30b0 47 5.50 81.74
## 7 a496d25e-8926-49ff-b1ab-6b944270a78a 33 3.86 85.60
## 8 2644c28d-dd59-4e26-b2be-58fc32e1e2f9 29 3.39 88.99
## 9 be8dc4f2-ebe8-4703-a0c1-f15a2eac4564 20 2.34 91.33
## 10 27223b7a-9667-4d59-b840-cd4e910541fd 10 1.17 92.50
## 11 7d32990a-6f26-4f12-87b5-98c68c2a5d23 10 1.17 93.67
## 12 a664523a-e041-47de-ab13-432b9c13c2a4 10 1.17 94.84
## 13 26b80cf4-a9f2-463f-8fd8-9930a99aaa04 8 0.94 95.78
## 14 ddc86eb2-9841-4d12-96ba-640b8888ce45 8 0.94 96.72
## 15 36c7ce93-71d9-4572-8842-f1859398d514 6 0.70 97.42
## 16 35b4bd7a-4b3b-4501-88a4-7cbe24349468 4 0.47 97.89
## 17 1be07fc1-2b81-43ea-8aa7-a540251286f7 3 0.35 98.24
## 18 3c1be397-349d-4788-b74e-bbdf0ba61e77 3 0.35 98.59
## 19 596edc61-94f5-4b57-86f9-27ed155f8a5f 3 0.35 98.94
## 20 62da543e-759e-4c1d-a8bc-c5ef80db18ca 3 0.35 99.29
## 21 71c53ed5-aa03-4b96-a511-3883c58942bd 1 0.12 99.41
## 22 900d961b-d818-4006-8073-1bc4e767e5b6 1 0.12 99.53
## 23 9bf1fc30-88df-4ad1-896b-ed5fa13d6d92 1 0.12 99.65
## 24 a3663320-c229-4efc-b972-ec20ccc7b93c 1 0.12 99.77
## 25 c0e12f9a-ee6f-426a-be24-392a641f43cc 1 0.12 99.89
## 26 f170f52d-0251-42f9-a0c4-b93d037823ab 1 0.12 100.00
## name.y frequency percentage cumulative_perc
## 1 Sin protección 275 32.16 32.16
## 2 <NA> 150 17.54 49.70
## 3 Ligera 85 9.94 59.64
## 4 Negativo 82 9.59 69.23
## 5 Moderada 68 7.95 77.18
## 6 Obstetricia y Ginecología 47 5.50 82.68
## 7 Protegidas 33 3.86 86.54
## 8 Intensa 29 3.39 89.93
## 9 Citrato de clomifeno 20 2.34 92.27
## 10 Medio 11 1.29 93.56
## 11 hCG 10 1.17 94.73
## 12 Laboratorio 10 1.17 95.90
## 13 Progesterona 10 1.17 97.07
## 14 Positivo 9 1.05 98.12
## 15 Familia práctica 8 0.94 99.06
## 16 Bajo 5 0.58 99.64
## 17 FSH 1 0.12 99.76
## 18 Fuerte 1 0.12 99.88
## 19 Pediatría 1 0.12 100.00
## num.y frequency percentage cumulative_perc
## 1 0 402 47.02 47.02
## 2 1 241 28.19 75.21
## 3 <NA> 150 17.54 92.75
## 4 2 32 3.74 96.49
## 5 4 20 2.34 98.83
## 6 3 10 1.17 100.00
## icon frequency percentage cumulative_perc
## 1 32 308 36.02 36.02
## 2 <NA> 150 17.54 53.56
## 3 -1 85 9.94 63.50
## 4 25 82 9.59 73.09
## 5 -2 68 7.95 81.04
## 6 21 66 7.72 88.76
## 7 31 41 4.80 93.56
## 8 -3 29 3.39 96.95
## 9 1 17 1.99 98.94
## 10 24 9 1.05 100.00
## color frequency percentage cumulative_perc
## 1 -65536 472 55.20 55.20
## 2 <NA> 150 17.54 72.74
## 3 -16711936 77 9.01 81.75
## 4 -1 66 7.72 89.47
## 5 -2601529 33 3.86 93.33
## 6 -16711681 20 2.34 95.67
## 7 -338673 11 1.29 96.96
## 8 -16776961 10 1.17 98.13
## 9 -65281 10 1.17 99.30
## 10 -256 6 0.70 100.00## [1] "Variables processed: date, symptom_id, name.x, num.x, mode, type, layout, chart, category, id.x, datetime, value, value_id, name.y, num.y, icon, color"describe(data)## data
##
## 17 Variables 855 Observations
## --------------------------------------------------------------------------------
## date
## n missing distinct
## 723 132 635
##
## lowest : 2012-09-09 2013-05-17 2013-05-29 2013-05-31 2013-06-05
## highest: 2020-05-14 2020-05-15 2020-05-26 2020-06-10 2020-06-11
## --------------------------------------------------------------------------------
## symptom_id
## n missing distinct
## 837 18 28
##
## lowest : 0 0239f552-c6b3-42ff-8005-0d45ea1f8962 0fa4d6da-91de-4504-a578-4bcc75d525c8 158f47bc-b2c5-463e-bffe-59db9423fe00 2a50ec7a-7d5c-462a-8004-076e552e84ec
## highest: cc0cc290-cbb2-464f-8174-9a5bce0fe639 ea62cb53-18f0-4f27-a236-8bd695926a8b f93bc06f-1489-410b-a02b-d0dd05d6fce5 fc56b772-6b14-45f3-aaec-76efe14f401d fd03ae70-ad11-4b31-ada8-f33ada07f92f
## --------------------------------------------------------------------------------
## name.x
## n missing distinct
## 836 19 27
##
## lowest : Acné cíclico Calambres abdominales Cervical apertura Citas médicas Día del ciclo
## highest: Posición cervical Prueba del embarazo Relación sexual Temperatura Test de ovulación
## --------------------------------------------------------------------------------
## num.x
## n missing distinct
## 836 19 27
##
## lowest : -8998 -8999 -9986 -9987 -9994, highest: 5 6 7 8 9
## --------------------------------------------------------------------------------
## mode
## n missing distinct
## 836 19 2
##
## Value 0 1
## Frequency 719 117
## Proportion 0.86 0.14
## --------------------------------------------------------------------------------
## type
## n missing distinct
## 836 19 9
##
## lowest : ° 1 2 4 5, highest: 5 6 7 8 9
##
## Value ° 1 2 4 5 6 7 8 9
## Frequency 17 408 78 308 1 8 1 1 14
## Proportion 0.020 0.488 0.093 0.368 0.001 0.010 0.001 0.001 0.017
## --------------------------------------------------------------------------------
## layout
## n missing distinct
## 836 19 19
##
## lowest : -1 0 1 2 2a50ec7a-7d5c-462a-8004-076e552e84ec
## highest: 8a63e2b4-61d7-43af-904d-9eff6e93392a 9c86dea7-6a8e-401f-9692-90e6ee0d23d7 cc0cc290-cbb2-464f-8174-9a5bce0fe639 ea62cb53-18f0-4f27-a236-8bd695926a8b fd03ae70-ad11-4b31-ada8-f33ada07f92f
## --------------------------------------------------------------------------------
## chart
## n missing distinct
## 836 19 22
##
## lowest : -1 0 0fa4d6da-91de-4504-a578-4bcc75d525c8 1 158f47bc-b2c5-463e-bffe-59db9423fe00
## highest: Insomnio Manchado / Sangrado Náuseas / vomitos Pechos sensibles Posición cervical
## --------------------------------------------------------------------------------
## category
## n missing distinct
## 836 19 27
##
## lowest : 0 0239f552-c6b3-42ff-8005-0d45ea1f8962 10 16 17
## highest: Moco cervical Ovulación prevista Peso Prueba del embarazo Test de ovulación
## --------------------------------------------------------------------------------
## id.x
## n missing distinct
## 117 738 117
##
## lowest : 009f6884-11f7-4d4e-8b1b-05c809f9be12 0141878b-80d4-44dc-8964-46bd870e39db 0292b053-4f55-4f77-97dc-91f7a6dc2341 03fbc39f-b17f-41cd-8d09-c1fb7e0f5951 0b992fff-4835-41a7-8a20-0813ea811087
## highest: f15a167e-5390-403f-a5c2-adb996ddda16 f573ddc8-7ca7-41ab-989b-508b2c77e5da f5b93dce-1016-4acf-bd0b-c602f9cc54d1 f775a24e-e538-4808-b96e-29960b8cc32a fd2ed856-ce1a-48ef-b2aa-8f117fbe4f5a
## --------------------------------------------------------------------------------
## datetime
## n missing distinct
## 117 738 117
##
## lowest : 2013-06-04T09:50:00 2013-06-09T10:02:00 2013-06-10T06:50:00 2013-06-14T09:15:00 2013-06-27T09:07:00
## highest: 2018-01-05T07:35:00 2018-07-04T11:50:00 2018-08-08T07:18:00 2018-11-24T09:43:00 2019-01-05T09:47:00
## --------------------------------------------------------------------------------
## value
## n missing distinct
## 117 738 62
##
## lowest : 36600 36700 36800 36900 37000, highest: 78200 78300 78500 79000 79900
## --------------------------------------------------------------------------------
## value_id
## n missing distinct
## 705 150 25
##
## lowest : 1be07fc1-2b81-43ea-8aa7-a540251286f7 23d86cea-d536-4343-ab59-a1bb86aec86e 2644c28d-dd59-4e26-b2be-58fc32e1e2f9 26b80cf4-a9f2-463f-8fd8-9930a99aaa04 27223b7a-9667-4d59-b840-cd4e910541fd
## highest: be8dc4f2-ebe8-4703-a0c1-f15a2eac4564 c0e12f9a-ee6f-426a-be24-392a641f43cc cc56c587-77d5-489e-a650-f7814c6f30b0 ddc86eb2-9841-4d12-96ba-640b8888ce45 f170f52d-0251-42f9-a0c4-b93d037823ab
## --------------------------------------------------------------------------------
## name.y
## n missing distinct
## 705 150 18
##
## lowest : Bajo Citrato de clomifeno Familia práctica FSH Fuerte
## highest: Pediatría Positivo Progesterona Protegidas Sin protección
##
## Bajo (5, 0.007), Citrato de clomifeno (20, 0.028), Familia práctica (8, 0.011),
## FSH (1, 0.001), Fuerte (1, 0.001), hCG (10, 0.014), Intensa (29, 0.041),
## Laboratorio (10, 0.014), Ligera (85, 0.121), Medio (11, 0.016), Moderada (68,
## 0.096), Negativo (82, 0.116), Obstetricia y Ginecología (47, 0.067), Pediatría
## (1, 0.001), Positivo (9, 0.013), Progesterona (10, 0.014), Protegidas (33,
## 0.047), Sin protección (275, 0.390)
## --------------------------------------------------------------------------------
## num.y
## n missing distinct
## 705 150 5
##
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##
## Value 0 1 2 3 4
## Frequency 402 241 32 10 20
## Proportion 0.570 0.342 0.045 0.014 0.028
## --------------------------------------------------------------------------------
## icon
## n missing distinct
## 705 150 9
##
## lowest : -1 -2 -3 1 21, highest: 21 24 25 31 32
##
## Value -1 -2 -3 1 21 24 25 31 32
## Frequency 85 68 29 17 66 9 82 41 308
## Proportion 0.121 0.096 0.041 0.024 0.094 0.013 0.116 0.058 0.437
## --------------------------------------------------------------------------------
## color
## n missing distinct
## 705 150 9
##
## lowest : -1 -16711681 -16711936 -16776961 -256
## highest: -256 -2601529 -338673 -65281 -65536
##
## Value -1 -16711681 -16711936 -16776961 -256 -2601529
## Frequency 66 20 77 10 6 33
## Proportion 0.094 0.028 0.109 0.014 0.009 0.047
##
## Value -338673 -65281 -65536
## Frequency 11 10 472
## Proportion 0.016 0.014 0.670
## --------------------------------------------------------------------------------dim(data)## [1] 855 17summary(data)## date symptom_id name.x num.x
## Length:855 Length:855 Length:855 Length:855
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## mode type layout chart
## Length:855 Length:855 Length:855 Length:855
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## category id.x datetime value
## Length:855 Length:855 Length:855 Length:855
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## value_id name.y num.y icon
## Length:855 Length:855 Length:855 Length:855
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## color
## Length:855
## Class :character
## Mode :charactercolnames(data)## [1] "date" "symptom_id" "name.x" "num.x" "mode"
## [6] "type" "layout" "chart" "category" "id.x"
## [11] "datetime" "value" "value_id" "name.y" "num.y"
## [16] "icon" "color"Obtenemos la fecha de datatime
data$Date <- lubridate::as_datetime(data$datetime)
data$date_ymd <- str_split_fixed(data$Date, " ", 2)Si en date es NA, copia el campo de date_ymd
data$date <- ifelse(is.na(data$date), data$date_ymd, data$date)Selecciono varias columnas
data <- select(data, -symptom_id, -value_id, -datetime, -value_id, -Date, -date_ymd)print("Mostrar variables con datos vacios")## [1] "Mostrar variables con datos vacios"colSums(data=="")## date name.x num.x mode type layout chart category
## 15 NA NA NA NA NA NA NA
## id.x value name.y num.y icon color
## NA NA NA NA NA NAEliminar filas con campos vacios y na
datos <- data[-which(data$date==""),]
df <- datos[!is.na(datos$name.x), ]
df$name.y[is.na(df$name.y)] <- "desconocido"
df[is.na(df)] <- 0datos <- select(df, -layout, -chart, -category, -id.x, -value)print("Suma valores na")## [1] "Suma valores na"sum(is.na(datos))## [1] 0print("Mostrar variables con campos na")## [1] "Mostrar variables con campos na"colSums(is.na(datos))## date name.x num.x mode type name.y num.y icon color
## 0 0 0 0 0 0 0 0 0df <- select(datos, -num.x, -mode, -num.y )
rm(data, data_raw, datos)df$date <- as.Date(df$date)
cols<-c("name.x","type","color","name.y", "icon")
for (i in cols){
df[,i] <- as.factor(df[,i])
}# ¿Con qué variables tendría sentido un proceso de discretización?
apply(df,2, function(x) length(unique(x)))## date name.x type name.y icon color
## 679 13 6 19 10 10colnames(df) <- c("date","categ_1", "categ_2", "categ_3", "categ_4", "categ_5")textscatter <- function(df, mapping, ...) {
ggplot(df, mapping, ...) + geom_text()
}
library(GGally)
ggpairs(
df,
title="Scatterplot de Variables",
columns = c(2,3,5,6),
mapping=ggplot2::aes(colour = categ_1))
lower = list(continuous = textscatter)skimr::skim(df)| Name | df |
| Number of rows | 822 |
| Number of columns | 6 |
| _______________________ | |
| Column type frequency: | |
| Date | 1 |
| factor | 5 |
| ________________________ | |
| Group variables | None |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| date | 0 | 1 | 2012-09-09 | 2020-06-11 | 2016-01-18 | 679 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| categ_1 | 0 | 1 | FALSE | 13 | Rel: 308, Men: 182, Pes: 100, Tes: 77 |
| categ_2 | 0 | 1 | FALSE | 6 | 1: 399, 4: 308, 2: 77, °: 17 |
| categ_3 | 0 | 1 | FALSE | 19 | Sin: 275, des: 117, Lig: 85, Neg: 82 |
| categ_4 | 0 | 1 | FALSE | 10 | 32: 308, 0: 117, -1: 85, 25: 82 |
| categ_5 | 0 | 1 | FALSE | 10 | -65: 472, 0: 117, -16: 77, -1: 66 |
list(df$categ_1[1:2])## [[1]]
## [1] Menstruación Citas médicas
## 13 Levels: Citas médicas Dolor muscular / articular ... Test de ovulaciónstr(df$categ_1)## Factor w/ 13 levels "Citas médicas",..: 6 1 11 1 11 11 1 11 1 11 ...levels(df$categ_1)## [1] "Citas médicas" "Dolor muscular / articular"
## [3] "Dolores de cabeza" "Manchado / Sangrado"
## [5] "Medicamentos" "Menstruación"
## [7] "Náuseas / vomitos" "Pechos sensibles"
## [9] "Peso" "Prueba del embarazo"
## [11] "Relación sexual" "Temperatura"
## [13] "Test de ovulación"summary(df$categ_1)## Citas médicas Dolor muscular / articular
## 66 1
## Dolores de cabeza Manchado / Sangrado
## 7 1
## Medicamentos Menstruación
## 41 182
## Náuseas / vomitos Pechos sensibles
## 4 4
## Peso Prueba del embarazo
## 100 14
## Relación sexual Temperatura
## 308 17
## Test de ovulación
## 77aed_basico <- function(data)
{
glimpse(data)
status(data) #library(git2r)
freq(data)
# profiling_num(data)
plot_num(data)
describe(data)
dim(data)
summary(data)
colnames(data)
str(data)
}aed_basico((df))## Rows: 822
## Columns: 6
## $ date <date> 2012-09-09, 2013-05-17, 2013-05-29, 2013-05-31, 2013-06-05, …
## $ categ_1 <fct> Menstruación, Citas médicas, Relación sexual, Citas médicas, …
## $ categ_2 <fct> 1, 1, 4, 1, 4, 4, 1, 4, 1, 4, 4, 1, 4, 4, 1, 1, 1, 1, 1, 4, 4…
## $ categ_3 <fct> Moderada, Obstetricia y Ginecología, Sin protección, Obstetri…
## $ categ_4 <fct> -2, 21, 32, 21, 32, 32, 21, 32, 21, 32, 32, -1, 32, 32, -2, -…
## $ categ_5 <fct> -65536, -1, -65536, -1, -65536, -65536, -1, -65536, -1, -2601…
## categ_1 frequency percentage cumulative_perc
## 1 Relación sexual 308 37.47 37.47
## 2 Menstruación 182 22.14 59.61
## 3 Peso 100 12.17 71.78
## 4 Test de ovulación 77 9.37 81.15
## 5 Citas médicas 66 8.03 89.18
## 6 Medicamentos 41 4.99 94.17
## 7 Temperatura 17 2.07 96.24
## 8 Prueba del embarazo 14 1.70 97.94
## 9 Dolores de cabeza 7 0.85 98.79
## 10 Náuseas / vomitos 4 0.49 99.28
## 11 Pechos sensibles 4 0.49 99.77
## 12 Dolor muscular / articular 1 0.12 99.89
## 13 Manchado / Sangrado 1 0.12 100.00
## categ_2 frequency percentage cumulative_perc
## 1 1 399 48.54 48.54
## 2 4 308 37.47 86.01
## 3 2 77 9.37 95.38
## 4 ° 17 2.07 97.45
## 5 9 14 1.70 99.15
## 6 6 7 0.85 100.00
## categ_3 frequency percentage cumulative_perc
## 1 Sin protección 275 33.45 33.45
## 2 desconocido 117 14.23 47.68
## 3 Ligera 85 10.34 58.02
## 4 Negativo 82 9.98 68.00
## 5 Moderada 68 8.27 76.27
## 6 Obstetricia y Ginecología 47 5.72 81.99
## 7 Protegidas 33 4.01 86.00
## 8 Intensa 29 3.53 89.53
## 9 Citrato de clomifeno 20 2.43 91.96
## 10 Medio 11 1.34 93.30
## 11 hCG 10 1.22 94.52
## 12 Laboratorio 10 1.22 95.74
## 13 Progesterona 10 1.22 96.96
## 14 Positivo 9 1.09 98.05
## 15 Familia práctica 8 0.97 99.02
## 16 Bajo 5 0.61 99.63
## 17 FSH 1 0.12 99.75
## 18 Fuerte 1 0.12 99.87
## 19 Pediatría 1 0.12 100.00
## categ_4 frequency percentage cumulative_perc
## 1 32 308 37.47 37.47
## 2 0 117 14.23 51.70
## 3 -1 85 10.34 62.04
## 4 25 82 9.98 72.02
## 5 -2 68 8.27 80.29
## 6 21 66 8.03 88.32
## 7 31 41 4.99 93.31
## 8 -3 29 3.53 96.84
## 9 1 17 2.07 98.91
## 10 24 9 1.09 100.00
## categ_5 frequency percentage cumulative_perc
## 1 -65536 472 57.42 57.42
## 2 0 117 14.23 71.65
## 3 -16711936 77 9.37 81.02
## 4 -1 66 8.03 89.05
## 5 -2601529 33 4.01 93.06
## 6 -16711681 20 2.43 95.49
## 7 -338673 11 1.34 96.83
## 8 -16776961 10 1.22 98.05
## 9 -65281 10 1.22 99.27
## 10 -256 6 0.73 100.00
## 'data.frame': 822 obs. of 6 variables:
## $ date : Date, format: "2012-09-09" "2013-05-17" ...
## $ categ_1: Factor w/ 13 levels "Citas médicas",..: 6 1 11 1 11 11 1 11 1 11 ...
## $ categ_2: Factor w/ 6 levels "°","1","2","4",..: 2 2 4 2 4 4 2 4 2 4 ...
## $ categ_3: Factor w/ 19 levels "Bajo","Citrato de clomifeno",..: 12 14 19 14 19 19 14 19 14 18 ...
## $ categ_4: Factor w/ 10 levels "-1","-2","-3",..: 2 6 10 6 10 10 6 10 6 10 ...
## $ categ_5: Factor w/ 10 levels "-1","-16711681",..: 9 1 9 1 9 9 1 9 1 6 ...#library(epiDisplay)
epiDisplay::codebook(df[2:6])##
##
##
## categ_1 :
## Frequency Percent
## Citas médicas 66 8.029
## Dolor muscular / articular 1 0.122
## Dolores de cabeza 7 0.852
## Manchado / Sangrado 1 0.122
## Medicamentos 41 4.988
## Menstruación 182 22.141
## Náuseas / vomitos 4 0.487
## Pechos sensibles 4 0.487
## Peso 100 12.165
## Prueba del embarazo 14 1.703
## Relación sexual 308 37.470
## Temperatura 17 2.068
## Test de ovulación 77 9.367
##
## ==================
## categ_2 :
## Frequency Percent
## ° 17 2.068
## 1 399 48.540
## 2 77 9.367
## 4 308 37.470
## 6 7 0.852
## 9 14 1.703
##
## ==================
## categ_3 :
## Frequency Percent
## Bajo 5 0.608
## Citrato de clomifeno 20 2.433
## desconocido 117 14.234
## Familia práctica 8 0.973
## FSH 1 0.122
## Fuerte 1 0.122
## hCG 10 1.217
## Intensa 29 3.528
## Laboratorio 10 1.217
## Ligera 85 10.341
## Medio 11 1.338
## Moderada 68 8.273
## Negativo 82 9.976
## Obstetricia y Ginecología 47 5.718
## Pediatría 1 0.122
## Positivo 9 1.095
## Progesterona 10 1.217
## Protegidas 33 4.015
## Sin protección 275 33.455
##
## ==================
## categ_4 :
## Frequency Percent
## -1 85 10.34
## -2 68 8.27
## -3 29 3.53
## 0 117 14.23
## 1 17 2.07
## 21 66 8.03
## 24 9 1.09
## 25 82 9.98
## 31 41 4.99
## 32 308 37.47
##
## ==================
## categ_5 :
## Frequency Percent
## -1 66 8.03
## -16711681 20 2.43
## -16711936 77 9.37
## -16776961 10 1.22
## -256 6 0.73
## -2601529 33 4.01
## -338673 11 1.34
## -65281 10 1.22
## -65536 472 57.42
## 0 117 14.23
##
## ==================epiDisplay::summ(df)##
## No. of observations = 822
##
## Var. name obs. mean median s.d. min. max.
## 1 date 822 16953.2615572 16818.5 <NA> 15592 18424
## 2 categ_1 822 8.617 10 3.379 1 13
## 3 categ_2 822 2.916 2 1.06 1 6
## 4 categ_3 822 12.762 13 5.912 1 19
## 5 categ_4 822 6.6 8 3.365 1 10
## 6 categ_5 822 7.518 9 2.917 1 10visdat::vis_dat(df)
# creo una columna de unos
ones<- matrix(1, 822, 1)df <- cbind(df,ones)
df$ones <- as.factor(df$ones)library(psych)
pairs.panels(df, pch=21,main="Matriz de Dispersión, Histograma y Correlación")
ts <- seq.POSIXt(as.POSIXct("2012-01-01",'%Y/%m/%d'), as.POSIXct("2020-06-20",'%Y/%m/%d'), by="day")
ts <- seq.POSIXt(as.POSIXlt("2012-01-01"), as.POSIXlt("2020-06-20"), by="day")
ts <- format.POSIXct(ts,'%Y/%m/%d')
dfa <- data.frame(timestamp=ts)X <- matrix(0, 3093, 6)
ddfa <- cbind(dfa,X)
colnames(ddfa)<- c("date", "categ_1", "categ_2", "categ_3", "categ_4", "categ_5","ones")
ddfa <- as.data.frame(ddfa)ddfa$date <- as.Date(ddfa$date)# Sustituyo valores NA en 0
df$ones[is.na(df$ones)] <- 0data <- full_join(ddfa,df) %>%
group_by(date) #%>%
#arrange(date())# Elimino las tablas que no necesito
rm(ddfa, df,dfa,lower,X,ones)ggplot( data = data, aes( date, categ_1 )) + geom_line() 
ggplot(data, aes(x=date, y=categ_1,color=categ_1)) + geom_line()
library(tidyr)
data_F <- spread(data, key = categ_1, value = ones)%>%
select(-categ_3,-categ_4,-categ_5)%>%
select("Menstruación","Pechos sensibles","Dolor muscular / articular","Dolores de cabeza", "Manchado / Sangrado")colnames(data_F) <- c("fecha", "menstruacion", "p_sensible", "dolor_muscular", "dolor_cabeza", "manchado")
#data_F( ,[2:6])[is.na(data_F( ,[2:6]))] <- 0
data_F$menstruacion[is.na(data_F$menstruacion)] <- 0
data_F$p_sensible[is.na(data_F$p_sensible)] <- 0
data_F$dolor_muscular[is.na(data_F$dolor_muscular)] <- 0
data_F$dolor_cabeza[is.na(data_F$dolor_cabeza)] <- 0
data_F$manchado[is.na(data_F$manchado)] <- 0#data_FF <- data_F%>%
# group_by(fecha)%>%
# summarise(menstruacion = sum(menstruacion),
# p_sensible = sum(p_sensible),
# dolor_muscular = sum(dolor_muscular),
# dolor_cabeza = sum(dolor_cabeza))%>%
# arrange(fecha)colss <- c("menstruacion", "p_sensible", "dolor_muscular", "dolor_cabeza", "manchado")
for (i in colss){
data_F[,i] <- as.numeric(unlist(data_F[,i]))
}# sustituyo 1 por 0 y 2 por 1
data_F[data_F == 1] <- 0
data_F[data_F == 2] <- 1
data_F$fecha <- as.Date(data_F$fecha)
head(data_F)## # A tibble: 6 x 6
## # Groups: fecha [6]
## fecha menstruacion p_sensible dolor_muscular dolor_cabeza manchado
## <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2012-01-01 0 0 0 0 0
## 2 2012-01-02 0 0 0 0 0
## 3 2012-01-03 0 0 0 0 0
## 4 2012-01-04 0 0 0 0 0
## 5 2012-01-05 0 0 0 0 0
## 6 2012-01-06 0 0 0 0 0data_F_mens=0
data_F_mens$difere=0
data_F_mens <- data_F %>%
select(fecha,menstruacion)%>%
group_by(fecha) %>%
filter(menstruacion == 1)%>%
arrange(fecha)
data_F_mens$difere=0
for (i in 2:nrow(data_F_mens)) {
data_F_mens$difere[i] <- difftime(data_F_mens$fecha[i], data_F_mens$fecha[i-1], units = "days")
}dataFmen <- data_F_mens %>%
dplyr::mutate(dates2=dmy(fecha),
year = lubridate::year(fecha),
month = lubridate::month(fecha),
day = lubridate::day(fecha))
dataFmen$date <-paste(dataFmen$month, dataFmen$year, sep="-")
dataFmen$dmy <- paste(dataFmen$year,dataFmen$month, dataFmen$day, sep="-")
#dataFmen$dia <- paste(dateFmen$day, dataFmen$month, dataFmen$year, sep = "-")
dataFmen$date## [1] "9-2012" "7-2013" "8-2013" "8-2013" "8-2013" "8-2013" "8-2013"
## [8] "9-2013" "9-2013" "9-2013" "9-2013" "7-2014" "8-2014" "8-2014"
## [15] "8-2014" "9-2014" "9-2014" "9-2014" "9-2014" "10-2014" "10-2014"
## [22] "10-2014" "10-2014" "12-2014" "12-2014" "12-2014" "12-2014" "12-2014"
## [29] "12-2014" "12-2014" "12-2014" "12-2014" "12-2014" "2-2015" "2-2015"
## [36] "2-2015" "2-2015" "2-2015" "3-2015" "3-2015" "3-2015" "3-2015"
## [43] "3-2015" "4-2015" "4-2015" "4-2015" "4-2015" "5-2015" "7-2015"
## [50] "7-2015" "7-2015" "7-2015" "7-2015" "7-2015" "7-2015" "9-2015"
## [57] "9-2015" "9-2015" "9-2015" "9-2015" "9-2015" "12-2015" "12-2015"
## [64] "12-2015" "12-2015" "12-2015" "1-2016" "1-2016" "1-2016" "1-2016"
## [71] "2-2016" "2-2016" "2-2016" "2-2016" "5-2016" "5-2016" "5-2016"
## [78] "5-2016" "2-2017" "3-2017" "3-2017" "3-2017" "3-2017" "3-2017"
## [85] "3-2017" "4-2017" "12-2017" "12-2017" "12-2017" "12-2017" "12-2017"
## [92] "12-2017" "12-2017" "12-2017" "12-2017" "2-2018" "2-2018" "2-2018"
## [99] "3-2018" "3-2018" "3-2018" "3-2018" "3-2018" "4-2018" "4-2018"
## [106] "4-2018" "4-2018" "4-2018" "5-2018" "5-2018" "5-2018" "5-2018"
## [113] "6-2018" "6-2018" "7-2018" "7-2018" "8-2018" "8-2018" "9-2018"
## [120] "9-2018" "10-2018" "10-2018" "10-2018" "11-2018" "11-2018" "11-2018"
## [127] "11-2018" "12-2018" "1-2019" "1-2019" "1-2019" "1-2019" "1-2019"
## [134] "2-2019" "2-2019" "2-2019" "2-2019" "2-2019" "2-2019" "2-2019"
## [141] "3-2019" "4-2019" "4-2019" "4-2019" "4-2019" "5-2019" "5-2019"
## [148] "6-2019" "6-2019" "6-2019" "6-2019" "6-2019" "7-2019" "7-2019"
## [155] "7-2019" "7-2019" "7-2019" "7-2019" "8-2019" "8-2019" "9-2019"
## [162] "9-2019" "10-2019" "11-2019" "11-2019" "11-2019" "11-2019" "11-2019"
## [169] "12-2019" "1-2020" "1-2020" "1-2020" "1-2020" "2-2020" "2-2020"
## [176] "3-2020" "5-2020" "5-2020" "5-2020" "5-2020" "6-2020" "6-2020"dataFmen$dmy <- as.Date(dataFmen$dmy)
DF <- dataFmen%>%
dplyr::select(difere)data_F_mensual <- dataFmen %>%
select(date,difere)%>%
group_by(date)%>%
summarise(dias=sum(difere))
data_F_mensual$date <- str_pad(data_F_mensual$date,width = 7, side="left", pad = "0")
library(zoo)
data_F_mensual$date <- zoo::as.yearmon(data_F_mensual$date, format = "%m-%Y")
data_F_mensual %>%
arrange(date)## # A tibble: 51 x 2
## date dias
## <yearmon> <dbl>
## 1 sep 2012 0
## 2 jul 2013 319
## 3 ago 2013 25
## 4 sep 2013 30
## 5 jul 2014 286
## 6 ago 2014 36
## 7 sep 2014 38
## 8 oct 2014 33
## 9 dic 2014 58
## 10 feb 2015 55
## # … with 41 more rowsrm(data, data_F, data_F_mens,dataFmen)ts <- seq.POSIXt(as.POSIXct("2012-01-01",'%Y/%m/%d'),
as.POSIXct("2020-07-01",'%Y/%m/%d'), by="month")
ts <- seq.POSIXt(as.POSIXlt("2012-01-01"), as.POSIXlt("2020-07-01"), by="month")
ts <- format.POSIXct(ts,'%Y/%m/%d')
dfa <- data.frame(timestamp=ts)
zero<- matrix(1, 103, 1)
df <- cbind(dfa,zero)
rm(dfa)
df$zero[df$zero == 1] <- 0
dataF <- df %>%
dplyr::mutate(year = lubridate::year(timestamp),
month = lubridate::month(timestamp),
day = lubridate::day(timestamp))
dataF$date <-paste(dataF$month, dataF$year, sep="-")
data_F_mensual_cero <- dataF %>%
select(date,zero)%>%
group_by(date)%>%
summarise(dias=sum(zero))
data_F_mensual_cero$date <- str_pad(data_F_mensual_cero$date,width = 7, side="left", pad = "0")
library(zoo)
data_F_mensual_cero$date <- zoo::as.yearmon(data_F_mensual_cero$date, format = "%m-%Y")
data_F_mensual_cero %>%
arrange(date)## # A tibble: 103 x 2
## date dias
## <yearmon> <dbl>
## 1 ene 2012 0
## 2 feb 2012 0
## 3 mar 2012 0
## 4 abr 2012 0
## 5 may 2012 0
## 6 jun 2012 0
## 7 jul 2012 0
## 8 ago 2012 0
## 9 sep 2012 0
## 10 oct 2012 0
## # … with 93 more rowsrm(dataF,df,dfa)
data_F_mensual$dias[data_F_mensual$dias == 0] <- 28
datos <- rbind(data_F_mensual,data_F_mensual_cero) %>%
arrange(date)
DATO <- datos%>%
group_by(date)%>%
summarise(valor=sum(dias))%>%
select(date,valor)
rm(data_F_mensual,data_F_mensual_cero,datos)df <- DATO %>%
dplyr::mutate(year = lubridate::year(date),
month = lubridate::month(date))%>%
arrange(month)
df$month <- str_pad(df$month, width = 2, side="left", pad = "0")
df$month2 <- str_sub(df$date,1,3)library(tidyquant)
df %>%
ggplot(aes(x = df$month, y = valor, group = year)) +
xlab(df$month2) +
geom_area(aes(fill = year), position = "stack") +
labs(title = "Grafica por mes/año", x = "mes", y = "valor",
subtitle = "Valores por mes y año") +
scale_y_continuous() +
theme_tq()
DATO$fecha <- as.character(DATO$date)
DATO %<>%
tidyr::separate(fecha, into = c("m", "y"), sep = " ") %>%
dplyr::mutate(y = as.numeric(y),
m = match(m, month.abb),
fechas = lubridate::make_date(y,m)) %>%
dplyr::select(-m, -y)pas1.ts <- ts(DATO["valor"], start = c(2012, 1), frequency = 12)
str(pas1.ts)## Time-Series [1:103, 1] from 2012 to 2020: 0 0 0 0 0 0 0 0 28 0 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr "valor"pas1.ts## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 2012 0 0 0 0 0 0 0 0 28 0 0 0
## 2013 0 0 0 0 0 0 319 25 30 0 0 0
## 2014 0 0 0 0 0 0 286 36 38 33 0 58
## 2015 0 55 33 34 47 0 57 0 62 0 0 77
## 2016 34 29 0 0 81 0 0 0 0 0 0 0
## 2017 0 275 50 9 0 0 0 0 0 0 0 268
## 2018 0 45 29 30 37 39 32 34 30 3 34 29
## 2019 34 31 28 38 27 39 34 27 31 32 32 27
## 2020 40 28 28 0 51 27 0autoplot(pas1.ts)
autoplot(pas1.ts, ts.colour = "red", ts.linetype = "dashed")
autoplot(pacf(pas1.ts, plot = FALSE))
autoplot(acf(pas1.ts, plot = FALSE), conf.int.fill = "#0000FF", conf.int.value = 0.8,conf.int.type = "ma")
autoplot(spec.ar(pas1.ts, plot = FALSE))
library(ggfortify)
#library(zoo)
library(forecast)
ggtsdiag(auto.arima(pas1.ts))
gglagplot(pas1.ts, lags = 4)
ggfreqplot(pas1.ts)
ggfreqplot(pas1.ts, freq = 4)
arima1<-forecast::auto.arima(pas1.ts)
forecast1<-forecast::forecast(arima1,level = c(95), h = 50)
autoplot(forecast1)
autoplot(forecast1, ts.colour = "firebrick1", predict.colour = "red",
predict.linetype = "dashed", conf.int = FALSE)
forecast::ggseasonplot(pas1.ts, year.labels=TRUE, year.labels.left=TRUE)
forecast::ggseasonplot(pas1.ts, year.labels=TRUE, year.labels.left=TRUE, polar = TRUE)
## .- Ruido Gaussiano linealmente dependiente en el tiempo
alpha <- 1
beta <- 0.1
t <- 1:103
mu <- alpha + beta*t
fit <- lm(pas1.ts ~ t) #calcula la regresión lm=modelo lineal
summary(fit) #slow y el interceptro y=ax+b##
## Call:
## lm(formula = pas1.ts ~ t)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.055 -25.167 -19.804 4.652 296.596
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.3170 11.1874 1.727 0.0873 .
## t 0.1625 0.1868 0.870 0.3863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 56.36 on 101 degrees of freedom
## Multiple R-squared: 0.007439, Adjusted R-squared: -0.002388
## F-statistic: 0.757 on 1 and 101 DF, p-value: 0.3863plot(fit)
dplyr::tibble(time = t, value = pas1.ts) %>%
ggplot2::ggplot(ggplot2::aes(x = time, y = value)) +
ggplot2::geom_line() +
ggplot2::geom_abline(intercept = fit$coefficients[1], slope = fit$coefficients[2], col = "red") #quiero calcular la lina roja con una regresión
# SOI= LA SERIE TEMPORAL
soi.lag6 <- xts::lag.xts(pas1.ts,6) # desplazar una serie temporal 6 veces, con lo que x1 es igual a y7, x2 igual a y8, x3 igual a y9....
fit <- lm(pas1.ts ~ soi.lag6) # esta es la relación que hace
summary(fit)##
## Call:
## lm(formula = pas1.ts ~ soi.lag6)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.005 -33.005 -4.005 5.004 285.995
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.0046 6.4887 5.087 1.83e-06 ***
## soi.lag6 -0.1253 0.1013 -1.236 0.219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 57.42 on 95 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.01583, Adjusted R-squared: 0.005469
## F-statistic: 1.528 on 1 and 95 DF, p-value: 0.2195dplyr::tibble(time = zoo::index(pas1.ts),
actual = zoo::coredata(pas1.ts),
estimated = c(rep(0,6),fit$fitted.values)) %>%
tidyr::gather(pas1.ts, Value, -time) %>%
ggplot2::ggplot(ggplot2::aes(x = time, y = Value, col = pas1.ts)) +
ggplot2::geom_line()
plot(fit)
# library(reshape2)
meltdf <- reshape2::melt(DATO,id="date")
ggplot(meltdf,aes(x=date,y=value,colour=variable,group=variable)) + geom_line()
## .- tslm rewritten
# autoplot of a forecast object
fc <- forecast::forecast(pas1.ts)
autoplot(fc)
# Plotting the components of an ETS model
fit <- forecast::ets(pas1.ts)
autoplot(fit)
# Plotting the inverse characteristic roots of an ARIMA model
fit <- forecast::auto.arima(pas1.ts, D=1)
autoplot(fit)
ggtsdisplay(pas1.ts)
ggseasonplot(pas1.ts)
menst.lm <- tslm(pas1.ts ~ trend + fourier(pas1.ts,3))
menst.fcast <- forecast(menst.lm,
data.frame(fourier(pas1.ts,3,36)))
autoplot(menst.fcast)
# camio los nombres de las columnas
names(DF)<-c("ds","y")
# sustituyo los valores 1 por 0
DF$y[DF$y == 1] <- 0
# incluyo un valor
DATO <-DF%>%
mutate(y = replace(y, ds=="2012-09-09", 29))
DATO <-DATO%>%
mutate(y = replace(y, ds=="2020-06-11", 29))
DATO <-DATO%>%
mutate(ds = replace(ds, ds=="2020-06-11", "2020-07-10"))
DF <- DATO %>%
filter(y>10)summary(DF)## ds y
## Min. :2012-09-09 Min. : 22.00
## 1st Qu.:2015-06-12 1st Qu.: 28.00
## Median :2018-04-26 Median : 31.00
## Mean :2017-07-15 Mean : 54.98
## 3rd Qu.:2019-06-07 3rd Qu.: 44.50
## Max. :2020-07-10 Max. :319.00# Calculo la moda
modeest::mfv(DF$y)## [1] 29m= prophet::prophet(DF)
m## $growth
## [1] "linear"
##
## $changepoints
## [1] "2013-08-16 GMT" "2013-09-15 GMT" "2014-08-04 GMT" "2014-09-07 GMT"
## [5] "2014-12-07 GMT" "2015-02-02 GMT" "2015-04-11 GMT" "2015-05-31 GMT"
## [9] "2015-09-22 GMT" "2016-01-13 GMT" "2016-02-11 GMT" "2017-02-04 GMT"
## [13] "2017-03-21 GMT" "2018-02-09 GMT" "2018-03-08 GMT" "2018-05-15 GMT"
## [17] "2018-07-27 GMT" "2018-08-30 GMT" "2018-11-03 GMT" "2018-12-05 GMT"
## [21] "2019-02-02 GMT" "2019-03-08 GMT" "2019-05-11 GMT" "2019-06-16 GMT"
## [25] "2019-08-19 GMT"
##
## $n.changepoints
## [1] 25
##
## $changepoint.range
## [1] 0.8
##
## $yearly.seasonality
## [1] "auto"
##
## $weekly.seasonality
## [1] "auto"
##
## $daily.seasonality
## [1] "auto"
##
## $holidays
## NULL
##
## $seasonality.mode
## [1] "additive"
##
## $seasonality.prior.scale
## [1] 10
##
## $changepoint.prior.scale
## [1] 0.05
##
## $holidays.prior.scale
## [1] 10
##
## $mcmc.samples
## [1] 0
##
## $interval.width
## [1] 0.8
##
## $uncertainty.samples
## [1] 1000
##
## $specified.changepoints
## [1] FALSE
##
## $start
## [1] "2012-09-09 GMT"
##
## $y.scale
## [1] 319
##
## $logistic.floor
## [1] FALSE
##
## $t.scale
## [1] 247190400
##
## $changepoints.t
## [1] 0.1191891 0.1296749 0.2425725 0.2544565 0.2862635 0.3061866 0.3299546
## [8] 0.3474310 0.3872772 0.4267739 0.4369102 0.5623908 0.5781195 0.6917162
## [15] 0.7011534 0.7249214 0.7504369 0.7623209 0.7850402 0.7962251 0.8168473
## [22] 0.8287312 0.8511010 0.8636840 0.8860538
##
## $seasonalities
## $seasonalities$yearly
## $seasonalities$yearly$period
## [1] 365.25
##
## $seasonalities$yearly$fourier.order
## [1] 10
##
## $seasonalities$yearly$prior.scale
## [1] 10
##
## $seasonalities$yearly$mode
## [1] "additive"
##
## $seasonalities$yearly$condition.name
## NULL
##
##
##
## $extra_regressors
## list()
##
## $country_holidays
## NULL
##
## $stan.fit
## $stan.fit$par
## $stan.fit$par$k
## [1] -0.3001667
##
## $stan.fit$par$m
## [1] 0.3472811
##
## $stan.fit$par$delta
## [1] -9.224371e-11 2.274530e-12 2.632662e-11 -6.417207e-11 -1.274390e-10
## [6] 1.328929e-10 -1.365647e-10 1.210806e-10 1.108463e-10 7.263403e-11
## [11] -5.459077e-11 1.126657e-10 -1.509415e-10 -1.387073e-10 3.288326e-12
## [16] -1.749745e-10 7.133282e-11 6.990091e-11 1.685207e-10 -1.081890e-11
## [21] -9.322807e-12 5.220184e-11 -1.604976e-10 1.812375e-10 -9.186081e-11
##
## $stan.fit$par$sigma_obs
## [1] 0.1736673
##
## $stan.fit$par$beta
## [1] 0.0106061277 -0.0006356418 -0.0184992633 0.0877641816 -0.0187050192
## [6] -0.0348382686 0.0168865974 0.0504891367 -0.0348728971 -0.0161552510
## [11] -0.0145480467 0.0304467588 -0.0394029539 -0.0101391044 -0.0723931696
## [16] 0.0254689558 -0.0428961392 0.0034579209 -0.0406740145 -0.0162581920
##
##
## $stan.fit$value
## [1] 62.70506
##
## $stan.fit$return_code
## [1] 0
##
## $stan.fit$theta_tilde
## k m delta[1] delta[2] delta[3] delta[4]
## [1,] -0.3001667 0.3472811 -9.224371e-11 2.27453e-12 2.632662e-11 -6.417207e-11
## delta[5] delta[6] delta[7] delta[8] delta[9]
## [1,] -1.27439e-10 1.328929e-10 -1.365647e-10 1.210806e-10 1.108463e-10
## delta[10] delta[11] delta[12] delta[13] delta[14]
## [1,] 7.263403e-11 -5.459077e-11 1.126657e-10 -1.509415e-10 -1.387073e-10
## delta[15] delta[16] delta[17] delta[18] delta[19]
## [1,] 3.288326e-12 -1.749745e-10 7.133282e-11 6.990091e-11 1.685207e-10
## delta[20] delta[21] delta[22] delta[23] delta[24]
## [1,] -1.08189e-11 -9.322807e-12 5.220184e-11 -1.604976e-10 1.812375e-10
## delta[25] sigma_obs beta[1] beta[2] beta[3] beta[4]
## [1,] -9.186081e-11 0.1736673 0.01060613 -0.0006356418 -0.01849926 0.08776418
## beta[5] beta[6] beta[7] beta[8] beta[9] beta[10]
## [1,] -0.01870502 -0.03483827 0.0168866 0.05048914 -0.0348729 -0.01615525
## beta[11] beta[12] beta[13] beta[14] beta[15] beta[16]
## [1,] -0.01454805 0.03044676 -0.03940295 -0.0101391 -0.07239317 0.02546896
## beta[17] beta[18] beta[19] beta[20]
## [1,] -0.04289614 0.003457921 -0.04067401 -0.01625819
##
##
## $params
## $params$k
## [1] -0.3001667
##
## $params$m
## [1] 0.3472811
##
## $params$delta
## [,1] [,2] [,3] [,4] [,5]
## [1,] -9.224371e-11 2.27453e-12 2.632662e-11 -6.417207e-11 -1.27439e-10
## [,6] [,7] [,8] [,9] [,10]
## [1,] 1.328929e-10 -1.365647e-10 1.210806e-10 1.108463e-10 7.263403e-11
## [,11] [,12] [,13] [,14] [,15]
## [1,] -5.459077e-11 1.126657e-10 -1.509415e-10 -1.387073e-10 3.288326e-12
## [,16] [,17] [,18] [,19] [,20]
## [1,] -1.749745e-10 7.133282e-11 6.990091e-11 1.685207e-10 -1.08189e-11
## [,21] [,22] [,23] [,24] [,25]
## [1,] -9.322807e-12 5.220184e-11 -1.604976e-10 1.812375e-10 -9.186081e-11
##
## $params$sigma_obs
## [1] 0.1736673
##
## $params$beta
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.01060613 -0.0006356418 -0.01849926 0.08776418 -0.01870502 -0.03483827
## [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] 0.0168866 0.05048914 -0.0348729 -0.01615525 -0.01454805 0.03044676
## [,13] [,14] [,15] [,16] [,17] [,18]
## [1,] -0.03940295 -0.0101391 -0.07239317 0.02546896 -0.04289614 0.003457921
## [,19] [,20]
## [1,] -0.04067401 -0.01625819
##
##
## $history
## # A tibble: 50 x 5
## # Groups: ds [50]
## ds y floor t y_scaled
## <dttm> <dbl> <dbl> <dbl> <dbl>
## 1 2012-09-09 00:00:00 29 0 0 0.0909
## 2 2013-07-25 00:00:00 319 0 0.111 1
## 3 2013-08-16 00:00:00 22 0 0.119 0.0690
## 4 2013-09-15 00:00:00 27 0 0.130 0.0846
## 5 2014-07-01 00:00:00 286 0 0.231 0.897
## 6 2014-08-04 00:00:00 34 0 0.243 0.107
## 7 2014-09-07 00:00:00 32 0 0.254 0.100
## 8 2014-10-13 00:00:00 30 0 0.267 0.0940
## 9 2014-12-07 00:00:00 52 0 0.286 0.163
## 10 2015-02-02 00:00:00 51 0 0.306 0.160
## # … with 40 more rows
##
## $history.dates
## [1] "2012-09-09 GMT" "2013-07-25 GMT" "2013-08-16 GMT" "2013-09-15 GMT"
## [5] "2014-07-01 GMT" "2014-08-04 GMT" "2014-09-07 GMT" "2014-10-13 GMT"
## [9] "2014-12-07 GMT" "2015-02-02 GMT" "2015-03-07 GMT" "2015-04-11 GMT"
## [13] "2015-05-31 GMT" "2015-07-21 GMT" "2015-09-22 GMT" "2015-12-09 GMT"
## [17] "2016-01-13 GMT" "2016-02-11 GMT" "2016-05-02 GMT" "2017-02-04 GMT"
## [21] "2017-03-21 GMT" "2017-12-20 GMT" "2018-02-09 GMT" "2018-03-08 GMT"
## [25] "2018-04-07 GMT" "2018-05-15 GMT" "2018-06-25 GMT" "2018-07-27 GMT"
## [29] "2018-08-30 GMT" "2018-09-30 GMT" "2018-11-03 GMT" "2018-12-05 GMT"
## [33] "2019-01-04 GMT" "2019-02-02 GMT" "2019-03-08 GMT" "2019-04-12 GMT"
## [37] "2019-05-11 GMT" "2019-06-16 GMT" "2019-07-19 GMT" "2019-08-19 GMT"
## [41] "2019-09-18 GMT" "2019-10-22 GMT" "2019-11-19 GMT" "2019-12-20 GMT"
## [45] "2020-01-26 GMT" "2020-02-25 GMT" "2020-03-25 GMT" "2020-05-12 GMT"
## [49] "2020-06-10 GMT" "2020-07-10 GMT"
##
## $train.holiday.names
## NULL
##
## $train.component.cols
## additive_terms yearly multiplicative_terms
## 1 1 1 0
## 2 1 1 0
## 3 1 1 0
## 4 1 1 0
## 5 1 1 0
## 6 1 1 0
## 7 1 1 0
## 8 1 1 0
## 9 1 1 0
## 10 1 1 0
## 11 1 1 0
## 12 1 1 0
## 13 1 1 0
## 14 1 1 0
## 15 1 1 0
## 16 1 1 0
## 17 1 1 0
## 18 1 1 0
## 19 1 1 0
## 20 1 1 0
##
## $component.modes
## $component.modes$additive
## [1] "yearly" "additive_terms"
## [3] "extra_regressors_additive" "holidays"
##
## $component.modes$multiplicative
## [1] "multiplicative_terms" "extra_regressors_multiplicative"
##
##
## $fit.kwargs
## list()
##
## attr(,"class")
## [1] "prophet" "list"# creamos el data.frame de base para el forecast con la función, incluida en el paquete, llamada make_future_dataframe. Y finalmente, usando la función genérica predict calculamos nuestro forecast.
future_df_prophet <- prophet::make_future_dataframe(m, periods = 24, freq = "months")
forecast <- predict(m, future_df_prophet)
tail(forecast[c("ds", "yhat", "yhat_lower","yhat_upper")], 24) # muestra de las últimas 12 filas de las columnas 1 y 2## ds yhat yhat_lower yhat_upper
## 51 2020-08-10 -12.227503 -85.150147 57.16422
## 52 2020-09-10 -32.810440 -105.012328 35.01267
## 53 2020-10-10 -2.657905 -76.563221 65.70874
## 54 2020-11-10 43.776963 -25.047073 115.74442
## 55 2020-12-10 36.382154 -37.945767 105.65894
## 56 2021-01-10 -30.901199 -102.480771 41.17990
## 57 2021-02-10 27.063977 -46.473923 93.85795
## 58 2021-03-10 -4.891745 -74.167857 69.30898
## 59 2021-04-10 -11.157906 -83.142239 57.71789
## 60 2021-05-10 17.995534 -51.917425 87.89040
## 61 2021-06-10 -5.819857 -72.138489 66.88218
## 62 2021-07-10 66.046576 -3.712635 133.67362
## 63 2021-08-10 -23.775603 -103.011335 46.05325
## 64 2021-09-10 -44.978123 -114.551199 27.43736
## 65 2021-10-10 -14.252917 -83.374498 61.93984
## 66 2021-11-10 31.773528 -39.876193 98.57483
## 67 2021-12-10 21.473568 -51.736128 94.07679
## 68 2022-01-10 -42.053386 -112.913042 27.87176
## 69 2022-02-10 16.013125 -48.968938 91.09247
## 70 2022-03-10 -17.481512 -89.258617 53.33024
## 71 2022-04-10 -23.162400 -96.630333 38.02565
## 72 2022-05-10 5.681424 -64.904666 76.34818
## 73 2022-06-10 -19.462706 -93.846008 54.62235
## 74 2022-07-10 54.310739 -14.080568 125.46050# vemos el resultado de la predicción.
plot(m ,forecast) 
prophet::prophet_plot_components(m ,forecast)
# Vemos la gráfica de forma interactiva
prophet::dyplot.prophet(m, forecast)##############################333
# Sustituyo los valores superioes a 200 por la moda
#library(modeest)
modeest::mfv(DF$y)## [1] 29DF <- DF%>%
mutate(y = replace(y, y>200, mfv(DF$y)))%>%
filter(y<200 & y>1)
#Indica el o los valores con más frecuenciam= prophet::prophet(DF)
m## $growth
## [1] "linear"
##
## $changepoints
## [1] "2013-08-16 GMT" "2013-09-15 GMT" "2014-08-04 GMT" "2014-09-07 GMT"
## [5] "2014-12-07 GMT" "2015-02-02 GMT" "2015-04-11 GMT" "2015-05-31 GMT"
## [9] "2015-09-22 GMT" "2016-01-13 GMT" "2016-02-11 GMT" "2017-02-04 GMT"
## [13] "2017-03-21 GMT" "2018-02-09 GMT" "2018-03-08 GMT" "2018-05-15 GMT"
## [17] "2018-07-27 GMT" "2018-08-30 GMT" "2018-11-03 GMT" "2018-12-05 GMT"
## [21] "2019-02-02 GMT" "2019-03-08 GMT" "2019-05-11 GMT" "2019-06-16 GMT"
## [25] "2019-08-19 GMT"
##
## $n.changepoints
## [1] 25
##
## $changepoint.range
## [1] 0.8
##
## $yearly.seasonality
## [1] "auto"
##
## $weekly.seasonality
## [1] "auto"
##
## $daily.seasonality
## [1] "auto"
##
## $holidays
## NULL
##
## $seasonality.mode
## [1] "additive"
##
## $seasonality.prior.scale
## [1] 10
##
## $changepoint.prior.scale
## [1] 0.05
##
## $holidays.prior.scale
## [1] 10
##
## $mcmc.samples
## [1] 0
##
## $interval.width
## [1] 0.8
##
## $uncertainty.samples
## [1] 1000
##
## $specified.changepoints
## [1] FALSE
##
## $start
## [1] "2012-09-09 GMT"
##
## $y.scale
## [1] 78
##
## $logistic.floor
## [1] FALSE
##
## $t.scale
## [1] 247190400
##
## $changepoints.t
## [1] 0.1191891 0.1296749 0.2425725 0.2544565 0.2862635 0.3061866 0.3299546
## [8] 0.3474310 0.3872772 0.4267739 0.4369102 0.5623908 0.5781195 0.6917162
## [15] 0.7011534 0.7249214 0.7504369 0.7623209 0.7850402 0.7962251 0.8168473
## [22] 0.8287312 0.8511010 0.8636840 0.8860538
##
## $seasonalities
## $seasonalities$yearly
## $seasonalities$yearly$period
## [1] 365.25
##
## $seasonalities$yearly$fourier.order
## [1] 10
##
## $seasonalities$yearly$prior.scale
## [1] 10
##
## $seasonalities$yearly$mode
## [1] "additive"
##
## $seasonalities$yearly$condition.name
## NULL
##
##
##
## $extra_regressors
## list()
##
## $country_holidays
## NULL
##
## $stan.fit
## $stan.fit$par
## $stan.fit$par$k
## [1] -0.1586665
##
## $stan.fit$par$m
## [1] 0.5416131
##
## $stan.fit$par$delta
## [1] 5.189815e-11 1.642905e-10 -1.698389e-10 -2.176398e-10 1.015737e-10
## [6] -8.268038e-07 -1.168132e-10 -3.043822e-10 -6.179129e-05 -3.975575e-10
## [11] -1.563962e-10 -2.596810e-06 2.742583e-11 -6.374238e-07 -1.413782e-10
## [16] -2.802557e-11 -7.520774e-11 1.048699e-10 6.899028e-11 1.716658e-10
## [21] -1.044479e-10 1.375717e-10 -1.184922e-10 -2.339773e-11 1.582198e-10
##
## $stan.fit$par$sigma_obs
## [1] 0.10544
##
## $stan.fit$par$beta
## [1] 0.054037318 -0.005424828 -0.076137909 -0.013143892 -0.007463079
## [6] 0.039317801 0.033791431 0.002184018 -0.038691056 -0.056908927
## [11] -0.006738086 0.003246190 0.032695694 -0.022666099 -0.021358230
## [16] -0.071768080 -0.030394700 0.015716300 0.065283467 -0.011015172
##
##
## $stan.fit$value
## [1] 87.44482
##
## $stan.fit$return_code
## [1] 0
##
## $stan.fit$theta_tilde
## k m delta[1] delta[2] delta[3] delta[4]
## [1,] -0.1586665 0.5416131 5.189815e-11 1.642905e-10 -1.698389e-10 -2.176398e-10
## delta[5] delta[6] delta[7] delta[8] delta[9]
## [1,] 1.015737e-10 -8.268038e-07 -1.168132e-10 -3.043822e-10 -6.179129e-05
## delta[10] delta[11] delta[12] delta[13] delta[14]
## [1,] -3.975575e-10 -1.563962e-10 -2.59681e-06 2.742583e-11 -6.374238e-07
## delta[15] delta[16] delta[17] delta[18] delta[19]
## [1,] -1.413782e-10 -2.802557e-11 -7.520774e-11 1.048699e-10 6.899028e-11
## delta[20] delta[21] delta[22] delta[23] delta[24]
## [1,] 1.716658e-10 -1.044479e-10 1.375717e-10 -1.184922e-10 -2.339773e-11
## delta[25] sigma_obs beta[1] beta[2] beta[3] beta[4]
## [1,] 1.582198e-10 0.10544 0.05403732 -0.005424828 -0.07613791 -0.01314389
## beta[5] beta[6] beta[7] beta[8] beta[9] beta[10]
## [1,] -0.007463079 0.0393178 0.03379143 0.002184018 -0.03869106 -0.05690893
## beta[11] beta[12] beta[13] beta[14] beta[15] beta[16]
## [1,] -0.006738086 0.00324619 0.03269569 -0.0226661 -0.02135823 -0.07176808
## beta[17] beta[18] beta[19] beta[20]
## [1,] -0.0303947 0.0157163 0.06528347 -0.01101517
##
##
## $params
## $params$k
## [1] -0.1586665
##
## $params$m
## [1] 0.5416131
##
## $params$delta
## [,1] [,2] [,3] [,4] [,5]
## [1,] 5.189815e-11 1.642905e-10 -1.698389e-10 -2.176398e-10 1.015737e-10
## [,6] [,7] [,8] [,9] [,10]
## [1,] -8.268038e-07 -1.168132e-10 -3.043822e-10 -6.179129e-05 -3.975575e-10
## [,11] [,12] [,13] [,14] [,15]
## [1,] -1.563962e-10 -2.59681e-06 2.742583e-11 -6.374238e-07 -1.413782e-10
## [,16] [,17] [,18] [,19] [,20]
## [1,] -2.802557e-11 -7.520774e-11 1.048699e-10 6.899028e-11 1.716658e-10
## [,21] [,22] [,23] [,24] [,25]
## [1,] -1.044479e-10 1.375717e-10 -1.184922e-10 -2.339773e-11 1.582198e-10
##
## $params$sigma_obs
## [1] 0.10544
##
## $params$beta
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.05403732 -0.005424828 -0.07613791 -0.01314389 -0.007463079 0.0393178
## [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] 0.03379143 0.002184018 -0.03869106 -0.05690893 -0.006738086 0.00324619
## [,13] [,14] [,15] [,16] [,17] [,18]
## [1,] 0.03269569 -0.0226661 -0.02135823 -0.07176808 -0.0303947 0.0157163
## [,19] [,20]
## [1,] 0.06528347 -0.01101517
##
##
## $history
## # A tibble: 50 x 5
## # Groups: ds [50]
## ds y floor t y_scaled
## <dttm> <dbl> <dbl> <dbl> <dbl>
## 1 2012-09-09 00:00:00 29 0 0 0.372
## 2 2013-07-25 00:00:00 29 0 0.111 0.372
## 3 2013-08-16 00:00:00 22 0 0.119 0.282
## 4 2013-09-15 00:00:00 27 0 0.130 0.346
## 5 2014-07-01 00:00:00 29 0 0.231 0.372
## 6 2014-08-04 00:00:00 34 0 0.243 0.436
## 7 2014-09-07 00:00:00 32 0 0.254 0.410
## 8 2014-10-13 00:00:00 30 0 0.267 0.385
## 9 2014-12-07 00:00:00 52 0 0.286 0.667
## 10 2015-02-02 00:00:00 51 0 0.306 0.654
## # … with 40 more rows
##
## $history.dates
## [1] "2012-09-09 GMT" "2013-07-25 GMT" "2013-08-16 GMT" "2013-09-15 GMT"
## [5] "2014-07-01 GMT" "2014-08-04 GMT" "2014-09-07 GMT" "2014-10-13 GMT"
## [9] "2014-12-07 GMT" "2015-02-02 GMT" "2015-03-07 GMT" "2015-04-11 GMT"
## [13] "2015-05-31 GMT" "2015-07-21 GMT" "2015-09-22 GMT" "2015-12-09 GMT"
## [17] "2016-01-13 GMT" "2016-02-11 GMT" "2016-05-02 GMT" "2017-02-04 GMT"
## [21] "2017-03-21 GMT" "2017-12-20 GMT" "2018-02-09 GMT" "2018-03-08 GMT"
## [25] "2018-04-07 GMT" "2018-05-15 GMT" "2018-06-25 GMT" "2018-07-27 GMT"
## [29] "2018-08-30 GMT" "2018-09-30 GMT" "2018-11-03 GMT" "2018-12-05 GMT"
## [33] "2019-01-04 GMT" "2019-02-02 GMT" "2019-03-08 GMT" "2019-04-12 GMT"
## [37] "2019-05-11 GMT" "2019-06-16 GMT" "2019-07-19 GMT" "2019-08-19 GMT"
## [41] "2019-09-18 GMT" "2019-10-22 GMT" "2019-11-19 GMT" "2019-12-20 GMT"
## [45] "2020-01-26 GMT" "2020-02-25 GMT" "2020-03-25 GMT" "2020-05-12 GMT"
## [49] "2020-06-10 GMT" "2020-07-10 GMT"
##
## $train.holiday.names
## NULL
##
## $train.component.cols
## additive_terms yearly multiplicative_terms
## 1 1 1 0
## 2 1 1 0
## 3 1 1 0
## 4 1 1 0
## 5 1 1 0
## 6 1 1 0
## 7 1 1 0
## 8 1 1 0
## 9 1 1 0
## 10 1 1 0
## 11 1 1 0
## 12 1 1 0
## 13 1 1 0
## 14 1 1 0
## 15 1 1 0
## 16 1 1 0
## 17 1 1 0
## 18 1 1 0
## 19 1 1 0
## 20 1 1 0
##
## $component.modes
## $component.modes$additive
## [1] "yearly" "additive_terms"
## [3] "extra_regressors_additive" "holidays"
##
## $component.modes$multiplicative
## [1] "multiplicative_terms" "extra_regressors_multiplicative"
##
##
## $fit.kwargs
## list()
##
## attr(,"class")
## [1] "prophet" "list"# creamos el data.frame de base para el forecast con la función, incluida en el paquete, llamada make_future_dataframe. Y finalmente, usando la función genérica predict calculamos nuestro forecast.
future_df_prophet <- prophet::make_future_dataframe(m, periods = 24, freq = "months")
forecast <- predict(m, future_df_prophet)
tail(forecast[c("ds", "yhat", "yhat_lower","yhat_upper")], 24) # muestra de las últimas 12 filas de las columnas 1 y 2## ds yhat yhat_lower yhat_upper
## 51 2020-08-10 19.48638 9.549298 30.88722
## 52 2020-09-10 22.86565 12.492999 33.61538
## 53 2020-10-10 23.13898 11.996523 34.63914
## 54 2020-11-10 24.43237 13.959990 35.05064
## 55 2020-12-10 42.14565 31.406383 53.10902
## 56 2021-01-10 26.30723 15.892670 37.03336
## 57 2021-02-10 27.50083 16.977758 38.43827
## 58 2021-03-10 26.59654 16.455268 37.46679
## 59 2021-04-10 25.71217 14.884137 36.14941
## 60 2021-05-10 43.00375 32.898733 53.36773
## 61 2021-06-10 36.31357 25.494012 47.65709
## 62 2021-07-10 30.89106 20.103845 41.32401
## 63 2021-08-10 17.80776 7.893730 28.11900
## 64 2021-09-10 21.07486 10.545432 31.10736
## 65 2021-10-10 21.63163 10.313179 31.48912
## 66 2021-11-10 23.01470 11.948248 33.83371
## 67 2021-12-10 40.75250 30.505222 51.18917
## 68 2022-01-10 24.51319 13.694734 35.18425
## 69 2022-02-10 26.04235 15.944304 36.68041
## 70 2022-03-10 24.76550 13.463140 35.12684
## 71 2022-04-10 23.72960 12.390996 33.88446
## 72 2022-05-10 42.17430 32.162158 52.70629
## 73 2022-06-10 34.81076 24.785612 45.39608
## 74 2022-07-10 29.11398 18.793087 39.58183# vemos el resultado de la predicción.
plot(m ,forecast) 
prophet::prophet_plot_components(m ,forecast)
# Vemos la gráfica de forma interactiva
prophet::dyplot.prophet(m, forecast)DF_1 <- DF%>%
filter(ds>"2012-09-01" & ds<"2016-03-01")m= prophet::prophet(DF_1)
m## $growth
## [1] "linear"
##
## $changepoints
## [1] "2013-07-25 GMT" "2013-08-16 GMT" "2013-09-15 GMT" "2014-07-01 GMT"
## [5] "2014-08-04 GMT" "2014-09-07 GMT" "2014-10-13 GMT" "2014-12-07 GMT"
## [9] "2015-02-02 GMT" "2015-03-07 GMT" "2015-04-11 GMT" "2015-05-31 GMT"
## [13] "2015-07-21 GMT"
##
## $n.changepoints
## [1] 13
##
## $changepoint.range
## [1] 0.8
##
## $yearly.seasonality
## [1] "auto"
##
## $weekly.seasonality
## [1] "auto"
##
## $daily.seasonality
## [1] "auto"
##
## $holidays
## NULL
##
## $seasonality.mode
## [1] "additive"
##
## $seasonality.prior.scale
## [1] 10
##
## $changepoint.prior.scale
## [1] 0.05
##
## $holidays.prior.scale
## [1] 10
##
## $mcmc.samples
## [1] 0
##
## $interval.width
## [1] 0.8
##
## $uncertainty.samples
## [1] 1000
##
## $specified.changepoints
## [1] FALSE
##
## $start
## [1] "2012-09-09 GMT"
##
## $y.scale
## [1] 73
##
## $logistic.floor
## [1] FALSE
##
## $t.scale
## [1] 1.08e+08
##
## $changepoints.t
## [1] 0.2552 0.2728 0.2968 0.5280 0.5552 0.5824 0.6112 0.6552 0.7008 0.7272
## [11] 0.7552 0.7952 0.8360
##
## $seasonalities
## $seasonalities$yearly
## $seasonalities$yearly$period
## [1] 365.25
##
## $seasonalities$yearly$fourier.order
## [1] 10
##
## $seasonalities$yearly$prior.scale
## [1] 10
##
## $seasonalities$yearly$mode
## [1] "additive"
##
## $seasonalities$yearly$condition.name
## NULL
##
##
##
## $extra_regressors
## list()
##
## $country_holidays
## NULL
##
## $stan.fit
## $stan.fit$par
## $stan.fit$par$k
## [1] 0.09949978
##
## $stan.fit$par$m
## [1] -0.9697463
##
## $stan.fit$par$delta
## [1] -4.890883e-02 -2.120526e-02 -6.466684e-02 1.697123e-07 -2.097848e-06
## [6] -1.685613e-06 -6.520004e-07 -2.265817e-06 -2.965650e-06 -5.230879e-07
## [11] -6.806423e-07 5.191480e-07 -9.567068e-07
##
## $stan.fit$par$sigma_obs
## [1] 2.870395e-10
##
## $stan.fit$par$beta
## [1] -2.07712374 2.08323364 2.42233885 2.71088275 -1.22720204 0.04487145
## [7] -2.58599962 3.09476643 0.34124285 0.11436197 -1.26468885 -1.49571114
## [13] 0.11230855 2.09529414 1.39565415 0.64508014 -2.21436230 0.74609986
## [19] -0.88600130 1.84517305
##
##
## $stan.fit$value
## [1] 392.4702
##
## $stan.fit$return_code
## [1] 0
##
## $stan.fit$theta_tilde
## k m delta[1] delta[2] delta[3] delta[4]
## [1,] 0.09949978 -0.9697463 -0.04890883 -0.02120526 -0.06466684 1.697123e-07
## delta[5] delta[6] delta[7] delta[8] delta[9]
## [1,] -2.097848e-06 -1.685613e-06 -6.520004e-07 -2.265817e-06 -2.96565e-06
## delta[10] delta[11] delta[12] delta[13] sigma_obs
## [1,] -5.230879e-07 -6.806423e-07 5.19148e-07 -9.567068e-07 2.870395e-10
## beta[1] beta[2] beta[3] beta[4] beta[5] beta[6] beta[7] beta[8]
## [1,] -2.077124 2.083234 2.422339 2.710883 -1.227202 0.04487145 -2.586 3.094766
## beta[9] beta[10] beta[11] beta[12] beta[13] beta[14] beta[15]
## [1,] 0.3412428 0.114362 -1.264689 -1.495711 0.1123086 2.095294 1.395654
## beta[16] beta[17] beta[18] beta[19] beta[20]
## [1,] 0.6450801 -2.214362 0.7460999 -0.8860013 1.845173
##
##
## $params
## $params$k
## [1] 0.09949978
##
## $params$m
## [1] -0.9697463
##
## $params$delta
## [,1] [,2] [,3] [,4] [,5]
## [1,] -0.04890883 -0.02120526 -0.06466684 1.697123e-07 -2.097848e-06
## [,6] [,7] [,8] [,9] [,10]
## [1,] -1.685613e-06 -6.520004e-07 -2.265817e-06 -2.96565e-06 -5.230879e-07
## [,11] [,12] [,13]
## [1,] -6.806423e-07 5.19148e-07 -9.567068e-07
##
## $params$sigma_obs
## [1] 2.870395e-10
##
## $params$beta
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] -2.077124 2.083234 2.422339 2.710883 -1.227202 0.04487145 -2.586 3.094766
## [,9] [,10] [,11] [,12] [,13] [,14] [,15]
## [1,] 0.3412428 0.114362 -1.264689 -1.495711 0.1123086 2.095294 1.395654
## [,16] [,17] [,18] [,19] [,20]
## [1,] 0.6450801 -2.214362 0.7460999 -0.8860013 1.845173
##
##
## $history
## # A tibble: 18 x 5
## # Groups: ds [18]
## ds y floor t y_scaled
## <dttm> <dbl> <dbl> <dbl> <dbl>
## 1 2012-09-09 00:00:00 29 0 0 0.397
## 2 2013-07-25 00:00:00 29 0 0.255 0.397
## 3 2013-08-16 00:00:00 22 0 0.273 0.301
## 4 2013-09-15 00:00:00 27 0 0.297 0.370
## 5 2014-07-01 00:00:00 29 0 0.528 0.397
## 6 2014-08-04 00:00:00 34 0 0.555 0.466
## 7 2014-09-07 00:00:00 32 0 0.582 0.438
## 8 2014-10-13 00:00:00 30 0 0.611 0.411
## 9 2014-12-07 00:00:00 52 0 0.655 0.712
## 10 2015-02-02 00:00:00 51 0 0.701 0.699
## 11 2015-03-07 00:00:00 29 0 0.727 0.397
## 12 2015-04-11 00:00:00 31 0 0.755 0.425
## 13 2015-05-31 00:00:00 47 0 0.795 0.644
## 14 2015-07-21 00:00:00 51 0 0.836 0.699
## 15 2015-09-22 00:00:00 57 0 0.886 0.781
## 16 2015-12-09 00:00:00 73 0 0.949 1
## 17 2016-01-13 00:00:00 31 0 0.977 0.425
## 18 2016-02-11 00:00:00 26 0 1 0.356
##
## $history.dates
## [1] "2012-09-09 GMT" "2013-07-25 GMT" "2013-08-16 GMT" "2013-09-15 GMT"
## [5] "2014-07-01 GMT" "2014-08-04 GMT" "2014-09-07 GMT" "2014-10-13 GMT"
## [9] "2014-12-07 GMT" "2015-02-02 GMT" "2015-03-07 GMT" "2015-04-11 GMT"
## [13] "2015-05-31 GMT" "2015-07-21 GMT" "2015-09-22 GMT" "2015-12-09 GMT"
## [17] "2016-01-13 GMT" "2016-02-11 GMT"
##
## $train.holiday.names
## NULL
##
## $train.component.cols
## additive_terms yearly multiplicative_terms
## 1 1 1 0
## 2 1 1 0
## 3 1 1 0
## 4 1 1 0
## 5 1 1 0
## 6 1 1 0
## 7 1 1 0
## 8 1 1 0
## 9 1 1 0
## 10 1 1 0
## 11 1 1 0
## 12 1 1 0
## 13 1 1 0
## 14 1 1 0
## 15 1 1 0
## 16 1 1 0
## 17 1 1 0
## 18 1 1 0
## 19 1 1 0
## 20 1 1 0
##
## $component.modes
## $component.modes$additive
## [1] "yearly" "additive_terms"
## [3] "extra_regressors_additive" "holidays"
##
## $component.modes$multiplicative
## [1] "multiplicative_terms" "extra_regressors_multiplicative"
##
##
## $fit.kwargs
## list()
##
## attr(,"class")
## [1] "prophet" "list"# creamos el data.frame de base para el forecast con la función, incluida en el paquete, llamada make_future_dataframe. Y finalmente, usando la función genérica predict calculamos nuestro forecast.
future_df_prophet <- prophet::make_future_dataframe(m, periods = 24, freq = "months")
forecast <- predict(m, future_df_prophet)
tail(forecast[c("ds", "yhat", "yhat_lower","yhat_upper")], 24) # muestra de las últimas 12 filas de las columnas 1 y 2## ds yhat yhat_lower yhat_upper
## 19 2016-03-11 82.437579 82.434582 82.439125
## 20 2016-04-11 47.315828 47.297339 47.327989
## 21 2016-05-11 -1425.139648 -1425.180881 -1425.107253
## 22 2016-06-11 47.929116 47.860656 47.989790
## 23 2016-07-11 114.492289 114.395044 114.584873
## 24 2016-08-11 32.488693 32.353761 32.614490
## 25 2016-09-11 26.366816 26.188915 26.531004
## 26 2016-10-11 85.583929 85.362753 85.780383
## 27 2016-11-11 -193.371254 -193.641020 -193.128024
## 28 2016-12-11 151.372355 151.057078 151.671937
## 29 2017-01-11 110.945243 110.577156 111.299344
## 30 2017-02-11 7.956236 7.538959 8.363113
## 31 2017-03-11 81.498486 81.026799 81.960543
## 32 2017-04-11 41.217268 40.676637 41.728632
## 33 2017-05-11 -1432.847058 -1433.463189 -1432.264400
## 34 2017-06-11 53.171472 52.489683 53.810056
## 35 2017-07-11 114.336561 113.583046 115.041235
## 36 2017-08-11 32.214324 31.396219 32.969179
## 37 2017-09-11 25.908334 25.006696 26.738517
## 38 2017-10-11 93.233158 92.270183 94.133631
## 39 2017-11-11 -210.425132 -211.439218 -209.465744
## 40 2017-12-11 141.226056 140.152999 142.255295
## 41 2018-01-11 127.234829 126.094206 128.331294
## 42 2018-02-11 13.130628 11.913848 14.300373# vemos el resultado de la predicción.
plot(m ,forecast) 
prophet::prophet_plot_components(m ,forecast)
# Vemos la gráfica de forma interactiva
prophet::dyplot.prophet(m, forecast)# Genero un dataset
prev_1<- data.frame(forecast$ds,forecast$yhat)
colnames(prev_1)<- c("ds", "y")
add_gap <- prev_1 %>%
dplyr::filter(ds>"2016-06-01" & ds<"2017-03-01")DF_cumpli <- rbind(DF, add_gap)%>%
arrange(ds)m= prophet::prophet(DF_cumpli)
m## $growth
## [1] "linear"
##
## $changepoints
## [1] "2013-08-16 GMT" "2014-07-01 GMT" "2014-09-07 GMT" "2014-10-13 GMT"
## [5] "2015-02-02 GMT" "2015-04-11 GMT" "2015-07-21 GMT" "2015-12-09 GMT"
## [9] "2016-02-11 GMT" "2016-05-02 GMT" "2016-07-11 GMT" "2016-09-11 GMT"
## [13] "2016-11-11 GMT" "2017-01-11 GMT" "2017-02-11 GMT" "2017-03-21 GMT"
## [17] "2018-02-09 GMT" "2018-04-07 GMT" "2018-06-25 GMT" "2018-08-30 GMT"
## [21] "2018-11-03 GMT" "2018-12-05 GMT" "2019-02-02 GMT" "2019-04-12 GMT"
## [25] "2019-06-16 GMT"
##
## $n.changepoints
## [1] 25
##
## $changepoint.range
## [1] 0.8
##
## $yearly.seasonality
## [1] "auto"
##
## $weekly.seasonality
## [1] "auto"
##
## $daily.seasonality
## [1] "auto"
##
## $holidays
## NULL
##
## $seasonality.mode
## [1] "additive"
##
## $seasonality.prior.scale
## [1] 10
##
## $changepoint.prior.scale
## [1] 0.05
##
## $holidays.prior.scale
## [1] 10
##
## $mcmc.samples
## [1] 0
##
## $interval.width
## [1] 0.8
##
## $uncertainty.samples
## [1] 1000
##
## $specified.changepoints
## [1] FALSE
##
## $start
## [1] "2012-09-09 GMT"
##
## $y.scale
## [1] 193.3713
##
## $logistic.floor
## [1] FALSE
##
## $t.scale
## [1] 247190400
##
## $changepoints.t
## [1] 0.1191891 0.2306886 0.2544565 0.2670395 0.3061866 0.3299546 0.3652569
## [8] 0.4145404 0.4369102 0.4652220 0.4896889 0.5113597 0.5326809 0.5540021
## [15] 0.5648375 0.5781195 0.6917162 0.7116393 0.7392520 0.7623209 0.7850402
## [22] 0.7962251 0.8168473 0.8409647 0.8636840
##
## $seasonalities
## $seasonalities$yearly
## $seasonalities$yearly$period
## [1] 365.25
##
## $seasonalities$yearly$fourier.order
## [1] 10
##
## $seasonalities$yearly$prior.scale
## [1] 10
##
## $seasonalities$yearly$mode
## [1] "additive"
##
## $seasonalities$yearly$condition.name
## NULL
##
##
##
## $extra_regressors
## list()
##
## $country_holidays
## NULL
##
## $stan.fit
## $stan.fit$par
## $stan.fit$par$k
## [1] 0.002243471
##
## $stan.fit$par$m
## [1] 0.173432
##
## $stan.fit$par$delta
## [1] -6.626886e-10 -7.779155e-10 3.478988e-11 -2.037687e-10 4.406802e-10
## [6] -4.808166e-10 5.124068e-10 -5.557317e-10 -3.192337e-10 -3.875548e-10
## [11] -6.543476e-10 5.495209e-10 3.506575e-10 5.245850e-10 -5.265448e-10
## [16] -5.168352e-10 2.265928e-10 -3.202373e-10 5.692246e-10 3.585422e-10
## [21] 1.222611e-11 -2.734093e-10 4.446057e-10 1.297125e-10 1.886397e-10
##
## $stan.fit$par$sigma_obs
## [1] 0.1457885
##
## $stan.fit$par$beta
## [1] 0.055532190 -0.034313373 0.021634502 0.024772392 0.030366055
## [6] 0.074414758 0.014223712 0.081583565 -0.073725121 0.001700364
## [11] -0.044045617 -0.020153719 0.010026689 -0.073045681 0.046356193
## [16] -0.075961792 0.019716738 -0.007718628 0.062488096 0.023439161
##
##
## $stan.fit$value
## [1] 84.26407
##
## $stan.fit$return_code
## [1] 0
##
## $stan.fit$theta_tilde
## k m delta[1] delta[2] delta[3]
## [1,] 0.002243471 0.173432 -6.626886e-10 -7.779155e-10 3.478988e-11
## delta[4] delta[5] delta[6] delta[7] delta[8]
## [1,] -2.037687e-10 4.406802e-10 -4.808166e-10 5.124068e-10 -5.557317e-10
## delta[9] delta[10] delta[11] delta[12] delta[13]
## [1,] -3.192337e-10 -3.875548e-10 -6.543476e-10 5.495209e-10 3.506575e-10
## delta[14] delta[15] delta[16] delta[17] delta[18]
## [1,] 5.24585e-10 -5.265448e-10 -5.168352e-10 2.265928e-10 -3.202373e-10
## delta[19] delta[20] delta[21] delta[22] delta[23]
## [1,] 5.692246e-10 3.585422e-10 1.222611e-11 -2.734093e-10 4.446057e-10
## delta[24] delta[25] sigma_obs beta[1] beta[2] beta[3]
## [1,] 1.297125e-10 1.886397e-10 0.1457885 0.05553219 -0.03431337 0.0216345
## beta[4] beta[5] beta[6] beta[7] beta[8] beta[9]
## [1,] 0.02477239 0.03036606 0.07441476 0.01422371 0.08158356 -0.07372512
## beta[10] beta[11] beta[12] beta[13] beta[14] beta[15]
## [1,] 0.001700364 -0.04404562 -0.02015372 0.01002669 -0.07304568 0.04635619
## beta[16] beta[17] beta[18] beta[19] beta[20]
## [1,] -0.07596179 0.01971674 -0.007718628 0.0624881 0.02343916
##
##
## $params
## $params$k
## [1] 0.002243471
##
## $params$m
## [1] 0.173432
##
## $params$delta
## [,1] [,2] [,3] [,4] [,5]
## [1,] -6.626886e-10 -7.779155e-10 3.478988e-11 -2.037687e-10 4.406802e-10
## [,6] [,7] [,8] [,9] [,10]
## [1,] -4.808166e-10 5.124068e-10 -5.557317e-10 -3.192337e-10 -3.875548e-10
## [,11] [,12] [,13] [,14] [,15]
## [1,] -6.543476e-10 5.495209e-10 3.506575e-10 5.24585e-10 -5.265448e-10
## [,16] [,17] [,18] [,19] [,20]
## [1,] -5.168352e-10 2.265928e-10 -3.202373e-10 5.692246e-10 3.585422e-10
## [,21] [,22] [,23] [,24] [,25]
## [1,] 1.222611e-11 -2.734093e-10 4.446057e-10 1.297125e-10 1.886397e-10
##
## $params$sigma_obs
## [1] 0.1457885
##
## $params$beta
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.05553219 -0.03431337 0.0216345 0.02477239 0.03036606 0.07441476
## [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] 0.01422371 0.08158356 -0.07372512 0.001700364 -0.04404562 -0.02015372
## [,13] [,14] [,15] [,16] [,17] [,18]
## [1,] 0.01002669 -0.07304568 0.04635619 -0.07596179 0.01971674 -0.007718628
## [,19] [,20]
## [1,] 0.0624881 0.02343916
##
##
## $history
## # A tibble: 59 x 5
## # Groups: ds [59]
## ds y floor t y_scaled
## <dttm> <dbl> <dbl> <dbl> <dbl>
## 1 2012-09-09 00:00:00 29 0 0 0.150
## 2 2013-07-25 00:00:00 29 0 0.111 0.150
## 3 2013-08-16 00:00:00 22 0 0.119 0.114
## 4 2013-09-15 00:00:00 27 0 0.130 0.140
## 5 2014-07-01 00:00:00 29 0 0.231 0.150
## 6 2014-08-04 00:00:00 34 0 0.243 0.176
## 7 2014-09-07 00:00:00 32 0 0.254 0.165
## 8 2014-10-13 00:00:00 30 0 0.267 0.155
## 9 2014-12-07 00:00:00 52 0 0.286 0.269
## 10 2015-02-02 00:00:00 51 0 0.306 0.264
## # … with 49 more rows
##
## $history.dates
## [1] "2012-09-09 GMT" "2013-07-25 GMT" "2013-08-16 GMT" "2013-09-15 GMT"
## [5] "2014-07-01 GMT" "2014-08-04 GMT" "2014-09-07 GMT" "2014-10-13 GMT"
## [9] "2014-12-07 GMT" "2015-02-02 GMT" "2015-03-07 GMT" "2015-04-11 GMT"
## [13] "2015-05-31 GMT" "2015-07-21 GMT" "2015-09-22 GMT" "2015-12-09 GMT"
## [17] "2016-01-13 GMT" "2016-02-11 GMT" "2016-05-02 GMT" "2016-06-11 GMT"
## [21] "2016-07-11 GMT" "2016-08-11 GMT" "2016-09-11 GMT" "2016-10-11 GMT"
## [25] "2016-11-11 GMT" "2016-12-11 GMT" "2017-01-11 GMT" "2017-02-04 GMT"
## [29] "2017-02-11 GMT" "2017-03-21 GMT" "2017-12-20 GMT" "2018-02-09 GMT"
## [33] "2018-03-08 GMT" "2018-04-07 GMT" "2018-05-15 GMT" "2018-06-25 GMT"
## [37] "2018-07-27 GMT" "2018-08-30 GMT" "2018-09-30 GMT" "2018-11-03 GMT"
## [41] "2018-12-05 GMT" "2019-01-04 GMT" "2019-02-02 GMT" "2019-03-08 GMT"
## [45] "2019-04-12 GMT" "2019-05-11 GMT" "2019-06-16 GMT" "2019-07-19 GMT"
## [49] "2019-08-19 GMT" "2019-09-18 GMT" "2019-10-22 GMT" "2019-11-19 GMT"
## [53] "2019-12-20 GMT" "2020-01-26 GMT" "2020-02-25 GMT" "2020-03-25 GMT"
## [57] "2020-05-12 GMT" "2020-06-10 GMT" "2020-07-10 GMT"
##
## $train.holiday.names
## NULL
##
## $train.component.cols
## additive_terms yearly multiplicative_terms
## 1 1 1 0
## 2 1 1 0
## 3 1 1 0
## 4 1 1 0
## 5 1 1 0
## 6 1 1 0
## 7 1 1 0
## 8 1 1 0
## 9 1 1 0
## 10 1 1 0
## 11 1 1 0
## 12 1 1 0
## 13 1 1 0
## 14 1 1 0
## 15 1 1 0
## 16 1 1 0
## 17 1 1 0
## 18 1 1 0
## 19 1 1 0
## 20 1 1 0
##
## $component.modes
## $component.modes$additive
## [1] "yearly" "additive_terms"
## [3] "extra_regressors_additive" "holidays"
##
## $component.modes$multiplicative
## [1] "multiplicative_terms" "extra_regressors_multiplicative"
##
##
## $fit.kwargs
## list()
##
## attr(,"class")
## [1] "prophet" "list"# creamos el data.frame de base para el forecast con la función, incluida en el paquete, llamada make_future_dataframe. Y finalmente, usando la función genérica predict calculamos nuestro forecast.
future_df_prophet <- prophet::make_future_dataframe(m, periods = 24, freq = "months")
forecast <- predict(m, future_df_prophet)
tail(forecast[c("ds", "yhat", "yhat_lower","yhat_upper")], 24) # muestra de las últimas 12 filas de las columnas 1 y 2## ds yhat yhat_lower yhat_upper
## 60 2020-08-10 28.98846 -6.291166 66.68334
## 61 2020-09-10 31.22575 -2.699680 69.18458
## 62 2020-10-10 51.16474 15.292941 86.68893
## 63 2020-11-10 -79.41561 -115.013667 -40.59499
## 64 2020-12-10 76.40277 40.326971 111.28939
## 65 2021-01-10 60.97660 25.958135 98.48667
## 66 2021-02-10 27.15102 -7.380600 64.71864
## 67 2021-03-10 32.91059 -2.350243 69.28745
## 68 2021-04-10 31.15229 -4.657612 67.11866
## 69 2021-05-10 44.93744 8.344344 81.06986
## 70 2021-06-10 41.67889 6.252154 78.92739
## 71 2021-07-10 61.42710 24.300094 96.58942
## 72 2021-08-10 28.85057 -4.563035 65.08214
## 73 2021-09-10 30.97644 -5.715006 66.79780
## 74 2021-10-10 50.68771 15.134411 86.81083
## 75 2021-11-10 -78.72696 -113.943429 -43.30127
## 76 2021-12-10 77.02958 41.033820 114.25004
## 77 2022-01-10 60.50087 22.101943 98.77115
## 78 2022-02-10 27.28942 -9.194622 61.59336
## 79 2022-03-10 32.60746 -4.129208 68.61344
## 80 2022-04-10 30.49903 -7.246273 66.16567
## 81 2022-05-10 45.90372 8.982406 83.25703
## 82 2022-06-10 42.17661 5.751824 77.82341
## 83 2022-07-10 61.37132 24.558183 98.44782# vemos el resultado de la predicción.
plot(m ,forecast) 
prophet::prophet_plot_components(m ,forecast)
# Vemos la gráfica de forma interactiva
prophet::dyplot.prophet(m, forecast)DF_2 <- DF_cumpli%>%
filter(ds>"2012-09-01" & ds<"2017-03-22")m= prophet::prophet(DF_2)
m## $growth
## [1] "linear"
##
## $changepoints
## [1] "2013-07-25 GMT" "2013-08-16 GMT" "2013-09-15 GMT" "2014-07-01 GMT"
## [5] "2014-08-04 GMT" "2014-09-07 GMT" "2014-10-13 GMT" "2014-12-07 GMT"
## [9] "2015-02-02 GMT" "2015-03-07 GMT" "2015-04-11 GMT" "2015-05-31 GMT"
## [13] "2015-07-21 GMT" "2015-09-22 GMT" "2015-12-09 GMT" "2016-01-13 GMT"
## [17] "2016-02-11 GMT" "2016-05-02 GMT" "2016-06-11 GMT" "2016-07-11 GMT"
## [21] "2016-08-11 GMT" "2016-09-11 GMT" "2016-10-11 GMT"
##
## $n.changepoints
## [1] 23
##
## $changepoint.range
## [1] 0.8
##
## $yearly.seasonality
## [1] "auto"
##
## $weekly.seasonality
## [1] "auto"
##
## $daily.seasonality
## [1] "auto"
##
## $holidays
## NULL
##
## $seasonality.mode
## [1] "additive"
##
## $seasonality.prior.scale
## [1] 10
##
## $changepoint.prior.scale
## [1] 0.05
##
## $holidays.prior.scale
## [1] 10
##
## $mcmc.samples
## [1] 0
##
## $interval.width
## [1] 0.8
##
## $uncertainty.samples
## [1] 1000
##
## $specified.changepoints
## [1] FALSE
##
## $start
## [1] "2012-09-09 GMT"
##
## $y.scale
## [1] 193.3713
##
## $logistic.floor
## [1] FALSE
##
## $t.scale
## [1] 142905600
##
## $changepoints.t
## [1] 0.1928658 0.2061669 0.2243047 0.3990326 0.4195889 0.4401451 0.4619105
## [8] 0.4951632 0.5296252 0.5495768 0.5707376 0.6009674 0.6318017 0.6698912
## [15] 0.7170496 0.7382104 0.7557437 0.8047158 0.8288996 0.8470375 0.8657799
## [22] 0.8845224 0.9026602
##
## $seasonalities
## $seasonalities$yearly
## $seasonalities$yearly$period
## [1] 365.25
##
## $seasonalities$yearly$fourier.order
## [1] 10
##
## $seasonalities$yearly$prior.scale
## [1] 10
##
## $seasonalities$yearly$mode
## [1] "additive"
##
## $seasonalities$yearly$condition.name
## NULL
##
##
##
## $extra_regressors
## list()
##
## $country_holidays
## NULL
##
## $stan.fit
## $stan.fit$par
## $stan.fit$par$k
## [1] 0.0004989296
##
## $stan.fit$par$m
## [1] 0.1356915
##
## $stan.fit$par$delta
## [1] 4.995401e-10 -5.766003e-10 -6.453863e-10 4.125884e-10 5.525516e-11
## [6] -6.011933e-10 -5.542843e-10 -5.574334e-10 -2.101130e-04 3.681295e-10
## [11] -1.552185e-11 -1.122043e-04 -1.380503e-11 -5.570376e-10 -7.964334e-10
## [16] -7.191333e-10 -9.072058e-10 -7.605276e-10 -1.377170e-09 -9.682689e-10
## [21] -2.072642e-11 4.248666e-10 -9.026364e-10
##
## $stan.fit$par$sigma_obs
## [1] 0.04421871
##
## $stan.fit$par$beta
## [1] 0.02259713 0.21335756 0.08451383 0.47899500 -0.33254056 0.68917428
## [7] -0.32329225 0.67754520 -0.39357294 -0.03103581 -0.29998060 0.05874224
## [13] 0.26713793 0.31787736 0.05083243 0.05144021 -0.65142264 0.46669723
## [19] -0.10675885 0.52890228
##
##
## $stan.fit$value
## [1] 78.55072
##
## $stan.fit$return_code
## [1] 0
##
## $stan.fit$theta_tilde
## k m delta[1] delta[2] delta[3]
## [1,] 0.0004989296 0.1356915 4.995401e-10 -5.766003e-10 -6.453863e-10
## delta[4] delta[5] delta[6] delta[7] delta[8]
## [1,] 4.125884e-10 5.525516e-11 -6.011933e-10 -5.542843e-10 -5.574334e-10
## delta[9] delta[10] delta[11] delta[12] delta[13]
## [1,] -0.000210113 3.681295e-10 -1.552185e-11 -0.0001122043 -1.380503e-11
## delta[14] delta[15] delta[16] delta[17] delta[18]
## [1,] -5.570376e-10 -7.964334e-10 -7.191333e-10 -9.072058e-10 -7.605276e-10
## delta[19] delta[20] delta[21] delta[22] delta[23]
## [1,] -1.37717e-09 -9.682689e-10 -2.072642e-11 4.248666e-10 -9.026364e-10
## sigma_obs beta[1] beta[2] beta[3] beta[4] beta[5] beta[6]
## [1,] 0.04421871 0.02259713 0.2133576 0.08451383 0.478995 -0.3325406 0.6891743
## beta[7] beta[8] beta[9] beta[10] beta[11] beta[12]
## [1,] -0.3232923 0.6775452 -0.3935729 -0.03103581 -0.2999806 0.05874224
## beta[13] beta[14] beta[15] beta[16] beta[17] beta[18] beta[19]
## [1,] 0.2671379 0.3178774 0.05083243 0.05144021 -0.6514226 0.4666972 -0.1067588
## beta[20]
## [1,] 0.5289023
##
##
## $params
## $params$k
## [1] 0.0004989296
##
## $params$m
## [1] 0.1356915
##
## $params$delta
## [,1] [,2] [,3] [,4] [,5]
## [1,] 4.995401e-10 -5.766003e-10 -6.453863e-10 4.125884e-10 5.525516e-11
## [,6] [,7] [,8] [,9] [,10]
## [1,] -6.011933e-10 -5.542843e-10 -5.574334e-10 -0.000210113 3.681295e-10
## [,11] [,12] [,13] [,14] [,15]
## [1,] -1.552185e-11 -0.0001122043 -1.380503e-11 -5.570376e-10 -7.964334e-10
## [,16] [,17] [,18] [,19] [,20]
## [1,] -7.191333e-10 -9.072058e-10 -7.605276e-10 -1.37717e-09 -9.682689e-10
## [,21] [,22] [,23]
## [1,] -2.072642e-11 4.248666e-10 -9.026364e-10
##
## $params$sigma_obs
## [1] 0.04421871
##
## $params$beta
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.02259713 0.2133576 0.08451383 0.478995 -0.3325406 0.6891743 -0.3232923
## [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## [1,] 0.6775452 -0.3935729 -0.03103581 -0.2999806 0.05874224 0.2671379 0.3178774
## [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 0.05083243 0.05144021 -0.6514226 0.4666972 -0.1067588 0.5289023
##
##
## $history
## # A tibble: 30 x 5
## # Groups: ds [30]
## ds y floor t y_scaled
## <dttm> <dbl> <dbl> <dbl> <dbl>
## 1 2012-09-09 00:00:00 29 0 0 0.150
## 2 2013-07-25 00:00:00 29 0 0.193 0.150
## 3 2013-08-16 00:00:00 22 0 0.206 0.114
## 4 2013-09-15 00:00:00 27 0 0.224 0.140
## 5 2014-07-01 00:00:00 29 0 0.399 0.150
## 6 2014-08-04 00:00:00 34 0 0.420 0.176
## 7 2014-09-07 00:00:00 32 0 0.440 0.165
## 8 2014-10-13 00:00:00 30 0 0.462 0.155
## 9 2014-12-07 00:00:00 52 0 0.495 0.269
## 10 2015-02-02 00:00:00 51 0 0.530 0.264
## # … with 20 more rows
##
## $history.dates
## [1] "2012-09-09 GMT" "2013-07-25 GMT" "2013-08-16 GMT" "2013-09-15 GMT"
## [5] "2014-07-01 GMT" "2014-08-04 GMT" "2014-09-07 GMT" "2014-10-13 GMT"
## [9] "2014-12-07 GMT" "2015-02-02 GMT" "2015-03-07 GMT" "2015-04-11 GMT"
## [13] "2015-05-31 GMT" "2015-07-21 GMT" "2015-09-22 GMT" "2015-12-09 GMT"
## [17] "2016-01-13 GMT" "2016-02-11 GMT" "2016-05-02 GMT" "2016-06-11 GMT"
## [21] "2016-07-11 GMT" "2016-08-11 GMT" "2016-09-11 GMT" "2016-10-11 GMT"
## [25] "2016-11-11 GMT" "2016-12-11 GMT" "2017-01-11 GMT" "2017-02-04 GMT"
## [29] "2017-02-11 GMT" "2017-03-21 GMT"
##
## $train.holiday.names
## NULL
##
## $train.component.cols
## additive_terms yearly multiplicative_terms
## 1 1 1 0
## 2 1 1 0
## 3 1 1 0
## 4 1 1 0
## 5 1 1 0
## 6 1 1 0
## 7 1 1 0
## 8 1 1 0
## 9 1 1 0
## 10 1 1 0
## 11 1 1 0
## 12 1 1 0
## 13 1 1 0
## 14 1 1 0
## 15 1 1 0
## 16 1 1 0
## 17 1 1 0
## 18 1 1 0
## 19 1 1 0
## 20 1 1 0
##
## $component.modes
## $component.modes$additive
## [1] "yearly" "additive_terms"
## [3] "extra_regressors_additive" "holidays"
##
## $component.modes$multiplicative
## [1] "multiplicative_terms" "extra_regressors_multiplicative"
##
##
## $fit.kwargs
## list()
##
## attr(,"class")
## [1] "prophet" "list"# creamos el data.frame de base para el forecast con la función, incluida en el paquete, llamada make_future_dataframe. Y finalmente, usando la función genérica predict calculamos nuestro forecast.
future_df_prophet <- prophet::make_future_dataframe(m, periods = 24, freq = "months")
forecast <- predict(m, future_df_prophet)
tail(forecast[c("ds", "yhat", "yhat_lower","yhat_upper")], 24) # muestra de las últimas 12 filas de las columnas 1 y 2## ds yhat yhat_lower yhat_upper
## 31 2017-04-21 403.30792 391.75389 413.73372
## 32 2017-05-21 -230.63348 -241.82102 -219.66729
## 33 2017-06-21 -42.84822 -53.84230 -31.38385
## 34 2017-07-21 54.67038 43.40812 65.45387
## 35 2017-08-21 22.20391 11.24206 32.91996
## 36 2017-09-21 53.45532 42.61872 64.10728
## 37 2017-10-21 -277.15863 -288.54733 -265.72059
## 38 2017-11-21 118.94677 108.73923 129.32450
## 39 2017-12-21 602.63903 591.88996 613.55340
## 40 2018-01-21 -209.18478 -220.08744 -198.34649
## 41 2018-02-21 -218.89401 -229.22165 -207.59393
## 42 2018-03-21 52.02913 40.74224 62.78733
## 43 2018-04-21 401.18588 390.82601 411.83413
## 44 2018-05-21 -237.86656 -248.66290 -226.27834
## 45 2018-06-21 -42.70666 -53.85921 -32.52039
## 46 2018-07-21 56.25263 46.21992 67.13284
## 47 2018-08-21 22.43700 11.34000 33.93788
## 48 2018-09-21 51.84868 40.47407 62.58102
## 49 2018-10-21 -266.84779 -277.97494 -255.85757
## 50 2018-11-21 115.84369 105.68905 127.74228
## 51 2018-12-21 591.39538 580.64154 603.19583
## 52 2019-01-21 -209.05615 -220.44573 -198.12786
## 53 2019-02-21 -215.95300 -225.99840 -204.87163
## 54 2019-03-21 59.69093 49.43177 70.82243# vemos el resultado de la predicción.
plot(m ,forecast) 
prophet::prophet_plot_components(m ,forecast)
# Vemos la gráfica de forma interactiva
prophet::dyplot.prophet(m, forecast)# Genero un dataset
prev_2<- data.frame(forecast$ds,forecast$yhat)
colnames(prev_2)<- c("ds", "y")
add_gap2 <- prev_2 %>%
dplyr::filter(ds>"2017-03-02" & ds<"2017-12-01")DF_cumplido <- rbind(DF_cumpli, add_gap2)%>%
arrange(ds)DF_cumplido <- data.frame(DF_cumplido)
boxplot(DF_cumplido$y)
# sustituimos los outliers por la media
mean(DF_cumplido$y)## [1] 33.1606# reemplazo de outliers con R
outliersReplace <- function(data, lowLimit, highLimit){
data[data < lowLimit] <- mean(data)
data[data > highLimit] <- median(data)
data #devolvemos el dato
}
DF_cumplido$ys <- outliersReplace(DF_cumplido$y, 20, 60)par(mfrow = c(1,2))
boxplot(DF_cumplido$y, main = "Sin reemplazo de outliers con R")
boxplot(DF_cumplido$ys, main = "Con reemplazo de outliers con R")
DF_cumplido <- DF_cumplido%>%
select(ds,ys)
colnames(DF_cumplido) <- c("ds","y")m= prophet::prophet(DF_cumplido)
m## $growth
## [1] "linear"
##
## $changepoints
## [1] "2013-08-16 GMT" "2014-07-01 GMT" "2014-09-07 GMT" "2014-12-07 GMT"
## [5] "2015-04-11 GMT" "2015-07-21 GMT" "2015-12-09 GMT" "2016-02-11 GMT"
## [9] "2016-06-11 GMT" "2016-08-11 GMT" "2016-10-11 GMT" "2016-12-11 GMT"
## [13] "2017-02-11 GMT" "2017-03-21 GMT" "2017-05-21 GMT" "2017-07-21 GMT"
## [17] "2017-09-21 GMT" "2017-11-21 GMT" "2018-02-09 GMT" "2018-04-07 GMT"
## [21] "2018-07-27 GMT" "2018-09-30 GMT" "2018-12-05 GMT" "2019-02-02 GMT"
## [25] "2019-04-12 GMT"
##
## $n.changepoints
## [1] 25
##
## $changepoint.range
## [1] 0.8
##
## $yearly.seasonality
## [1] "auto"
##
## $weekly.seasonality
## [1] "auto"
##
## $daily.seasonality
## [1] "auto"
##
## $holidays
## NULL
##
## $seasonality.mode
## [1] "additive"
##
## $seasonality.prior.scale
## [1] 10
##
## $changepoint.prior.scale
## [1] 0.05
##
## $holidays.prior.scale
## [1] 10
##
## $mcmc.samples
## [1] 0
##
## $interval.width
## [1] 0.8
##
## $uncertainty.samples
## [1] 1000
##
## $specified.changepoints
## [1] FALSE
##
## $start
## [1] "2012-09-09 GMT"
##
## $y.scale
## [1] 57
##
## $logistic.floor
## [1] FALSE
##
## $t.scale
## [1] 247190400
##
## $changepoints.t
## [1] 0.1191891 0.2306886 0.2544565 0.2862635 0.3299546 0.3652569 0.4145404
## [8] 0.4369102 0.4792031 0.5005243 0.5218455 0.5431667 0.5648375 0.5781195
## [15] 0.5994408 0.6207620 0.6424327 0.6637539 0.6917162 0.7116393 0.7504369
## [22] 0.7731562 0.7962251 0.8168473 0.8409647
##
## $seasonalities
## $seasonalities$yearly
## $seasonalities$yearly$period
## [1] 365.25
##
## $seasonalities$yearly$fourier.order
## [1] 10
##
## $seasonalities$yearly$prior.scale
## [1] 10
##
## $seasonalities$yearly$mode
## [1] "additive"
##
## $seasonalities$yearly$condition.name
## NULL
##
##
##
## $extra_regressors
## list()
##
## $country_holidays
## NULL
##
## $stan.fit
## $stan.fit$par
## $stan.fit$par$k
## [1] -0.1170377
##
## $stan.fit$par$m
## [1] 0.6529306
##
## $stan.fit$par$delta
## [1] -8.438936e-11 6.705918e-11 -1.748139e-10 9.647235e-11 -1.028489e-07
## [6] -7.455531e-11 -2.091698e-10 -2.633186e-10 -8.578937e-11 -4.563319e-11
## [11] -1.598237e-10 -3.807855e-10 -1.505891e-11 2.037823e-12 -4.228461e-08
## [16] -9.021332e-10 -5.124983e-10 -1.855701e-10 -2.030914e-10 -1.826039e-10
## [21] -1.582009e-10 5.939575e-11 -2.110264e-10 1.261452e-10 -1.137176e-10
##
## $stan.fit$par$sigma_obs
## [1] 0.1209844
##
## $stan.fit$par$beta
## [1] 1.783405e-02 -1.829823e-02 -3.538345e-02 3.208659e-03 3.250552e-02
## [6] -1.638247e-02 3.286684e-03 1.607049e-02 -9.799664e-03 -2.038322e-03
## [11] 4.552217e-02 -6.092417e-02 -7.290247e-03 -1.670439e-03 -2.402819e-02
## [16] -2.214170e-02 -9.990058e-03 8.592668e-05 3.267693e-02 -1.846748e-02
##
##
## $stan.fit$value
## [1] 109.5539
##
## $stan.fit$return_code
## [1] 0
##
## $stan.fit$theta_tilde
## k m delta[1] delta[2] delta[3] delta[4]
## [1,] -0.1170377 0.6529306 -8.438936e-11 6.705918e-11 -1.748139e-10 9.647235e-11
## delta[5] delta[6] delta[7] delta[8] delta[9]
## [1,] -1.028489e-07 -7.455531e-11 -2.091698e-10 -2.633186e-10 -8.578937e-11
## delta[10] delta[11] delta[12] delta[13] delta[14]
## [1,] -4.563319e-11 -1.598237e-10 -3.807855e-10 -1.505891e-11 2.037823e-12
## delta[15] delta[16] delta[17] delta[18] delta[19]
## [1,] -4.228461e-08 -9.021332e-10 -5.124983e-10 -1.855701e-10 -2.030914e-10
## delta[20] delta[21] delta[22] delta[23] delta[24]
## [1,] -1.826039e-10 -1.582009e-10 5.939575e-11 -2.110264e-10 1.261452e-10
## delta[25] sigma_obs beta[1] beta[2] beta[3] beta[4]
## [1,] -1.137176e-10 0.1209844 0.01783405 -0.01829823 -0.03538345 0.003208659
## beta[5] beta[6] beta[7] beta[8] beta[9] beta[10]
## [1,] 0.03250552 -0.01638247 0.003286684 0.01607049 -0.009799664 -0.002038322
## beta[11] beta[12] beta[13] beta[14] beta[15] beta[16]
## [1,] 0.04552217 -0.06092417 -0.007290247 -0.001670439 -0.02402819 -0.0221417
## beta[17] beta[18] beta[19] beta[20]
## [1,] -0.009990058 8.592668e-05 0.03267693 -0.01846748
##
##
## $params
## $params$k
## [1] -0.1170377
##
## $params$m
## [1] 0.6529306
##
## $params$delta
## [,1] [,2] [,3] [,4] [,5]
## [1,] -8.438936e-11 6.705918e-11 -1.748139e-10 9.647235e-11 -1.028489e-07
## [,6] [,7] [,8] [,9] [,10]
## [1,] -7.455531e-11 -2.091698e-10 -2.633186e-10 -8.578937e-11 -4.563319e-11
## [,11] [,12] [,13] [,14] [,15]
## [1,] -1.598237e-10 -3.807855e-10 -1.505891e-11 2.037823e-12 -4.228461e-08
## [,16] [,17] [,18] [,19] [,20]
## [1,] -9.021332e-10 -5.124983e-10 -1.855701e-10 -2.030914e-10 -1.826039e-10
## [,21] [,22] [,23] [,24] [,25]
## [1,] -1.582009e-10 5.939575e-11 -2.110264e-10 1.261452e-10 -1.137176e-10
##
## $params$sigma_obs
## [1] 0.1209844
##
## $params$beta
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.01783405 -0.01829823 -0.03538345 0.003208659 0.03250552 -0.01638247
## [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] 0.003286684 0.01607049 -0.009799664 -0.002038322 0.04552217 -0.06092417
## [,13] [,14] [,15] [,16] [,17] [,18]
## [1,] -0.007290247 -0.001670439 -0.02402819 -0.0221417 -0.009990058 8.592668e-05
## [,19] [,20]
## [1,] 0.03267693 -0.01846748
##
##
## $history
## ds y floor t y_scaled
## 1 2012-09-09 29.00000 0 0.0000000 0.5087719
## 2 2013-07-25 29.00000 0 0.1114995 0.5087719
## 3 2013-08-16 22.00000 0 0.1191891 0.3859649
## 4 2013-09-15 27.00000 0 0.1296749 0.4736842
## 5 2014-07-01 29.00000 0 0.2306886 0.5087719
## 6 2014-08-04 34.00000 0 0.2425725 0.5964912
## 7 2014-09-07 32.00000 0 0.2544565 0.5614035
## 8 2014-10-13 30.00000 0 0.2670395 0.5263158
## 9 2014-12-07 52.00000 0 0.2862635 0.9122807
## 10 2015-02-02 51.00000 0 0.3061866 0.8947368
## 11 2015-03-07 29.00000 0 0.3177211 0.5087719
## 12 2015-04-11 31.00000 0 0.3299546 0.5438596
## 13 2015-05-31 47.00000 0 0.3474310 0.8245614
## 14 2015-07-21 51.00000 0 0.3652569 0.8947368
## 15 2015-09-22 57.00000 0 0.3872772 1.0000000
## 16 2015-12-09 32.00000 0 0.4145404 0.5614035
## 17 2016-01-13 31.00000 0 0.4267739 0.5438596
## 18 2016-02-11 26.00000 0 0.4369102 0.4561404
## 19 2016-05-02 32.00000 0 0.4652220 0.5614035
## 20 2016-06-11 47.92912 0 0.4792031 0.8408617
## 21 2016-07-11 32.00000 0 0.4896889 0.5614035
## 22 2016-08-11 32.48869 0 0.5005243 0.5699771
## 23 2016-09-11 26.36682 0 0.5113597 0.4625757
## 24 2016-10-11 32.00000 0 0.5218455 0.5614035
## 25 2016-11-11 33.16060 0 0.5326809 0.5817650
## 26 2016-12-11 32.00000 0 0.5431667 0.5614035
## 27 2017-01-11 32.00000 0 0.5540021 0.5614035
## 28 2017-02-04 29.00000 0 0.5623908 0.5087719
## 29 2017-02-11 33.16060 0 0.5648375 0.5817650
## 30 2017-03-21 45.00000 0 0.5781195 0.7894737
## 31 2017-03-21 44.21367 0 0.5781195 0.7756783
## 32 2017-04-21 32.00000 0 0.5889549 0.5614035
## 33 2017-05-21 33.16060 0 0.5994408 0.5817650
## 34 2017-06-21 33.16060 0 0.6102761 0.5817650
## 35 2017-07-21 54.67038 0 0.6207620 0.9591295
## 36 2017-08-21 22.20391 0 0.6315973 0.3895424
## 37 2017-09-21 53.45532 0 0.6424327 0.9378125
## 38 2017-10-21 33.16060 0 0.6529186 0.5817650
## 39 2017-11-21 32.00000 0 0.6637539 0.5614035
## 40 2017-12-20 29.00000 0 0.6738902 0.5087719
## 41 2018-02-09 43.00000 0 0.6917162 0.7543860
## 42 2018-03-08 25.00000 0 0.7011534 0.4385965
## 43 2018-04-07 26.00000 0 0.7116393 0.4561404
## 44 2018-05-15 34.00000 0 0.7249214 0.5964912
## 45 2018-06-25 38.00000 0 0.7392520 0.6666667
## 46 2018-07-27 31.00000 0 0.7504369 0.5438596
## 47 2018-08-30 33.00000 0 0.7623209 0.5789474
## 48 2018-09-30 29.00000 0 0.7731562 0.5087719
## 49 2018-11-03 31.00000 0 0.7850402 0.5438596
## 50 2018-12-05 29.00000 0 0.7962251 0.5087719
## 51 2019-01-04 30.00000 0 0.8067109 0.5263158
## 52 2019-02-02 25.00000 0 0.8168473 0.4385965
## 53 2019-03-08 28.00000 0 0.8287312 0.4912281
## 54 2019-04-12 35.00000 0 0.8409647 0.6140351
## 55 2019-05-11 26.00000 0 0.8511010 0.4561404
## 56 2019-06-16 35.00000 0 0.8636840 0.6140351
## 57 2019-07-19 29.00000 0 0.8752185 0.5087719
## 58 2019-08-19 26.00000 0 0.8860538 0.4561404
## 59 2019-09-18 29.00000 0 0.8965397 0.5087719
## 60 2019-10-22 32.00000 0 0.9084236 0.5614035
## 61 2019-11-19 28.00000 0 0.9182104 0.4912281
## 62 2019-12-20 27.00000 0 0.9290458 0.4736842
## 63 2020-01-26 37.00000 0 0.9419783 0.6491228
## 64 2020-02-25 27.00000 0 0.9524642 0.4736842
## 65 2020-03-25 28.00000 0 0.9626005 0.4912281
## 66 2020-05-12 48.00000 0 0.9793778 0.8421053
## 67 2020-06-10 26.00000 0 0.9895142 0.4561404
## 68 2020-07-10 29.00000 0 1.0000000 0.5087719
##
## $history.dates
## [1] "2012-09-09 GMT" "2013-07-25 GMT" "2013-08-16 GMT" "2013-09-15 GMT"
## [5] "2014-07-01 GMT" "2014-08-04 GMT" "2014-09-07 GMT" "2014-10-13 GMT"
## [9] "2014-12-07 GMT" "2015-02-02 GMT" "2015-03-07 GMT" "2015-04-11 GMT"
## [13] "2015-05-31 GMT" "2015-07-21 GMT" "2015-09-22 GMT" "2015-12-09 GMT"
## [17] "2016-01-13 GMT" "2016-02-11 GMT" "2016-05-02 GMT" "2016-06-11 GMT"
## [21] "2016-07-11 GMT" "2016-08-11 GMT" "2016-09-11 GMT" "2016-10-11 GMT"
## [25] "2016-11-11 GMT" "2016-12-11 GMT" "2017-01-11 GMT" "2017-02-04 GMT"
## [29] "2017-02-11 GMT" "2017-03-21 GMT" "2017-03-21 GMT" "2017-04-21 GMT"
## [33] "2017-05-21 GMT" "2017-06-21 GMT" "2017-07-21 GMT" "2017-08-21 GMT"
## [37] "2017-09-21 GMT" "2017-10-21 GMT" "2017-11-21 GMT" "2017-12-20 GMT"
## [41] "2018-02-09 GMT" "2018-03-08 GMT" "2018-04-07 GMT" "2018-05-15 GMT"
## [45] "2018-06-25 GMT" "2018-07-27 GMT" "2018-08-30 GMT" "2018-09-30 GMT"
## [49] "2018-11-03 GMT" "2018-12-05 GMT" "2019-01-04 GMT" "2019-02-02 GMT"
## [53] "2019-03-08 GMT" "2019-04-12 GMT" "2019-05-11 GMT" "2019-06-16 GMT"
## [57] "2019-07-19 GMT" "2019-08-19 GMT" "2019-09-18 GMT" "2019-10-22 GMT"
## [61] "2019-11-19 GMT" "2019-12-20 GMT" "2020-01-26 GMT" "2020-02-25 GMT"
## [65] "2020-03-25 GMT" "2020-05-12 GMT" "2020-06-10 GMT" "2020-07-10 GMT"
##
## $train.holiday.names
## NULL
##
## $train.component.cols
## additive_terms yearly multiplicative_terms
## 1 1 1 0
## 2 1 1 0
## 3 1 1 0
## 4 1 1 0
## 5 1 1 0
## 6 1 1 0
## 7 1 1 0
## 8 1 1 0
## 9 1 1 0
## 10 1 1 0
## 11 1 1 0
## 12 1 1 0
## 13 1 1 0
## 14 1 1 0
## 15 1 1 0
## 16 1 1 0
## 17 1 1 0
## 18 1 1 0
## 19 1 1 0
## 20 1 1 0
##
## $component.modes
## $component.modes$additive
## [1] "yearly" "additive_terms"
## [3] "extra_regressors_additive" "holidays"
##
## $component.modes$multiplicative
## [1] "multiplicative_terms" "extra_regressors_multiplicative"
##
##
## $fit.kwargs
## list()
##
## attr(,"class")
## [1] "prophet" "list"# creamos el data.frame de base para el forecast con la función, incluida en el paquete, llamada make_future_dataframe. Y finalmente, usando la función genérica predict calculamos nuestro forecast.
future_df_prophet <- prophet::make_future_dataframe(m, periods =1, freq = "months")
forecast <- predict(m, future_df_prophet)
tail(forecast[c("ds", "yhat", "yhat_lower","yhat_upper")], 1) # muestra de las últimas 12 filas de las columnas 1 y 2## ds yhat yhat_lower yhat_upper
## 69 2020-08-10 25.50266 17.10546 34.38513# vemos el resultado de la predicción.
plot(m ,forecast) 
prophet::prophet_plot_components(m ,forecast)
# Vemos la gráfica de forma interactiva
prophet::dyplot.prophet(m, forecast)